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ORCIDB. John Oommen
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- affiliation: Carleton University, Ottawa, Canada
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2020 – today
- 2024
[j191]Rebekka Olsson Omslandseter
, Lei Jiao, Xuan Zhang, Anis Yazidi, B. John Oommen:
The Hierarchical Discrete Pursuit Learning Automaton: A Novel Scheme With Fast Convergence and Epsilon-Optimality. IEEE Trans. Neural Networks Learn. Syst. 35(6): 8278-8292 (2024)- 2023
- [j190]Rebekka Olsson Omslandseter, Lei Jiao, B. John Oommen:
Pioneering approaches for enhancing the speed of hierarchical LA by ordering the actions. Inf. Sci. 647: 119487 (2023) - [j189]Ismail Hassan, B. John Oommen, Anis Yazidi:
Adaptive learning with artificial barriers yielding Nash equilibria in general games. Knowl. Eng. Rev. 38 (2023) - [j188]Rebekka Olsson Omslandseter, Lei Jiao, Yuanwei Liu, B. John Oommen:
User grouping and power allocation in NOMA systems: a novel semi-supervised reinforcement learning-based solution. Pattern Anal. Appl. 26(1): 1-17 (2023) - [j187]B. John Oommen, Rebekka Olsson Omslandseter, Lei Jiao:
Learning automata-based partitioning algorithms for stochastic grouping problems with non-equal partition sizes. Pattern Anal. Appl. 26(2): 751-772 (2023) - [j186]B. John Oommen, Rebekka Olsson Omslandseter, Lei Jiao:
The object migration automata: its field, scope, applications, and future research challenges. Pattern Anal. Appl. 26(3): 917-928 (2023) - [j185]Anis Yazidi, Daniel Silvestre, B. John Oommen:
Solving Two-Person Zero-Sum Stochastic Games With Incomplete Information Using Learning Automata With Artificial Barriers. IEEE Trans. Neural Networks Learn. Syst. 34(2): 650-661 (2023) - 2022
- [c199]Rebekka Olsson Omslandseter, Lei Jiao, Xuan Zhang, Anis Yazidi, B. John Oommen:
The Hierarchical Discrete Learning Automaton Suitable for Environments with Many Actions and High Accuracy Requirements. AI 2022: 507-518 - [c198]Rebekka Olsson Omslandseter, Lei Jiao, B. John Oommen:
Enhancing the Speed of Hierarchical Learning Automata by Ordering the Actions - A Pioneering Approach. AI 2022: 775-788 - [c197]B. John Oommen, Xuan Zhang, Lei Jiao:
A Comprehensive Survey of Estimator Learning Automata and Their Recent Convergence Results. Honoring Professor Mohammad S. Obaidat 2022: 33-52 - [c196]Ismail Hassan, B. John Oommen, Anis Yazidi:
Learning Automata with Artificial Reflecting Barriers in Games with Limited Information. FLAIRS 2022 - [i2]Ismail Hassan, Anis Yazidi, B. John Oommen:
Adaptive Learning with Artificial Barriers Yielding Nash Equilibria in General Games. CoRR abs/2203.15780 (2022) - 2021
- [j184]Omar Ghaleb, B. John Oommen:
On solving single elevator-like problems using a learning automata-based paradigm. Evol. Syst. 12(1): 37-56 (2021) - [j183]O. Ekaba Bisong, B. John Oommen:
On utilizing an enhanced object partitioning scheme to optimize self-organizing lists-on-lists. Evol. Syst. 12(1): 123-154 (2021) - [j182]O. Ekaba Bisong, B. John Oommen:
On utilizing the transitivity pursuit-enhanced object partitioning to optimize self-organizing lists-on-lists. Evol. Syst. 12(3): 655-686 (2021) - [j181]Fatemeh Mahmoudi, Mostafa Razmkhah, B. John Oommen:
Nonparametric "anti-Bayesian" quantile-based pattern classification. Pattern Anal. Appl. 24(1): 75-87 (2021) - [j180]Tahira Ghani, B. John Oommen:
On utilizing 2D features from 3D scans to enhance the prediction of lung cancer survival rates. Pattern Recognit. Lett. 152: 56-62 (2021) - [j179]Anis Yazidi, Ismail Hassan, Hugo Lewi Hammer, B. John Oommen:
Achieving Fair Load Balancing by Invoking a Learning Automata-Based Two-Time-Scale Separation Paradigm. IEEE Trans. Neural Networks Learn. Syst. 32(8): 3444-3457 (2021) - [c195]Rebekka Olsson Omslandseter, Lei Jiao, B. John Oommen:
A Learning-Automata Based Solution for Non-equal Partitioning: Partitions with Common GCD Sizes. IEA/AIE (2) 2021: 227-239 - [c194]Rebekka Olsson Omslandseter, Lei Jiao, B. John Oommen:
Object Migration Automata for Non-equal Partitioning Problems with Known Partition Sizes. AIAI 2021: 129-142 - 2020
- [j178]Abdolreza Shirvani, B. John Oommen:
On enhancing the deadlock-preventing object migration automaton using the pursuit paradigm. Pattern Anal. Appl. 23(2): 509-526 (2020) - [j177]Xuan Zhang, Lei Jiao, B. John Oommen, Ole-Christoffer Granmo:
A Conclusive Analysis of the Finite-Time Behavior of the Discretized Pursuit Learning Automaton. IEEE Trans. Neural Networks Learn. Syst. 31(1): 284-294 (2020) - [j176]Anis Yazidi, Xuan Zhang, Lei Jiao, B. John Oommen:
The Hierarchical Continuous Pursuit Learning Automation: A Novel Scheme for Environments With Large Numbers of Actions. IEEE Trans. Neural Networks Learn. Syst. 31(2): 512-526 (2020) - [c193]Tahira Ghani, B. John Oommen:
Novel Block Diagonalization for Reducing Features and Computations in Medical Diagnosis. Australasian Conference on Artificial Intelligence 2020: 42-54 - [c192]Tahira Ghani, B. John Oommen:
Enhancing the Prediction of Lung Cancer Survival Rates Using 2D Features from 3D Scans. ICIAR (2) 2020: 202-215 - [c191]Rebekka Olsson Omslandseter, Lei Jiao, Yuanwei Liu, B. John Oommen:
User Grouping and Power Allocation in NOMA Systems: A Reinforcement Learning-Based Solution. IEA/AIE 2020: 299-311 - [c190]Ibrahim Helmy, B. John Oommen:
A Novel Learning Automata-Based Strategy to Generate Melodies from Chordal Inputs. AIAI (1) 2020: 203-215 - [c189]O. Ekaba Bisong, B. John Oommen:
Optimizing Self-organizing Lists-on-Lists Using Transitivity and Pursuit-Enhanced Object Partitioning. AIAI (1) 2020: 227-240
2010 – 2019
- 2019
- [j175]Hanane Tavasoli, B. John Oommen, Anis Yazidi:
On utilizing weak estimators to achieve the online classification of data streams. Eng. Appl. Artif. Intell. 86: 11-31 (2019) - [c188]Nicolas Perez, B. John Oommen:
Multi-Minimax: A New AI Paradigm for Simultaneously-Played Multi-player Games. Australasian Conference on Artificial Intelligence 2019: 41-53 - [c187]Abdolreza Shirvani, B. John Oommen:
The Power of the "Pursuit" Learning Paradigm in the Partitioning of Data. EANN 2019: 3-16 - [c186]Omar Ghaleb, B. John Oommen:
Learning Automata-Based Solutions to the Multi-Elevator Problem. ICIC (3) 2019: 130-141 - [c185]O. Ekaba Bisong, B. John Oommen:
Optimizing Self-organizing Lists-on-Lists Using Pursuit-Oriented Enhanced Object Partitioning. ICIC (3) 2019: 201-212 - [c184]Jessica Havelock, B. John Oommen, Ole-Christoffer Granmo:
On Using "Stochastic Learning on the Line" to Design Novel Distance Estimation Methods for Three-Dimensional Environments. IEA/AIE 2019: 39-49 - [c183]Abdolreza Shirvani, B. John Oommen:
The Power of the "Pursuit" Learning Paradigm in the Partitioning of Data. AIAI 2019: 3-16 - [c182]Omar Ghaleb, B. John Oommen:
Learning Automata-Based Solutions to the Single Elevator Problem. AIAI 2019: 439-450 - [c181]O. Ekaba Bisong, B. John Oommen:
Optimizing Self-organizing Lists-on-Lists Using Enhanced Object Partitioning. AIAI 2019: 451-463 - 2018
- [j174]Rajan Thapa, Lei Jiao, B. John Oommen, Anis Yazidi:
A Learning Automaton-Based Scheme for Scheduling Domestic Shiftable Loads in Smart Grids. IEEE Access 6: 5348-5361 (2018) - [j173]Abdolreza Shirvani, B. John Oommen:
On Invoking Transitivity to Enhance the Pursuit-Oriented Object Migration Automata. IEEE Access 6: 21668-21681 (2018) - [j172]Anis Yazidi, Hugo Hammer, B. John Oommen:
Higher-Fidelity Frugal and Accurate Quantile Estimation Using a Novel Incremental Discretized Paradigm. IEEE Access 6: 24362-24374 (2018) - [j171]Jessica Havelock, B. John Oommen, Ole-Christoffer Granmo:
Novel Distance Estimation Methods Using "Stochastic Learning on the Line" Strategies. IEEE Access 6: 48438-48454 (2018) - [j170]Spencer Polk, B. John Oommen:
Challenging state-of-the-art move ordering with Adaptive Data Structures. Appl. Intell. 48(5): 1128-1147 (2018) - [j169]Spencer Polk, B. John Oommen:
Novel threat-based AI strategies that incorporate adaptive data structures for multi-player board games. Appl. Intell. 48(8): 1893-1911 (2018) - [j168]Ratish Mohan, Anis Yazidi, Boning Feng, B. John Oommen:
On optimizing firewall performance in dynamic networks by invoking a novel swapping window-based paradigm. Int. J. Commun. Syst. 31(15) (2018) - [j167]Akaki Jobava, Anis Yazidi, B. John Oommen, Kyrre M. Begnum:
On achieving intelligent traffic-aware consolidation of virtual machines in a data center using Learning Automata. J. Comput. Sci. 24: 290-312 (2018) - [j166]Abdolreza Shirvani, B. John Oommen:
On enhancing the object migration automaton using the Pursuit paradigm. J. Comput. Sci. 24: 329-342 (2018) - [j165]Hugo Lewi Hammer, Anis Yazidi, B. John Oommen:
On the classification of dynamical data streams using novel "Anti-Bayesian" techniques. Pattern Recognit. 76: 108-124 (2018) - [c180]Jessica Havelock, B. John Oommen, Ole-Christoffer Granmo:
On Using "Stochastic Learning on the Line" to Design Novel Distance Estimation Methods. IEA/AIE 2018: 34-42 - [c179]Armando H. Taucer, Spencer Polk, B. John Oommen:
On Addressing the Challenges of Complex Stochastic Games Using "Representative" Moves. AIAI 2018: 3-13 - [c178]Anis Yazidi, Xuan Zhang, Lei Jiao, B. John Oommen:
The Hierarchical Continuous Pursuit Learning Automation for Large Numbers of Actions. AIAI 2018: 451-461 - 2017
- [j164]Nathan Bell, B. John Oommen:
A novel abstraction for swarm intelligence: particle field optimization. Auton. Agents Multi Agent Syst. 31(2): 362-385 (2017) - [j163]B. John Oommen, Sang-Woon Kim:
Occlusion-based estimation of independent multinomial random variables using occurrence and sequential information. Eng. Appl. Artif. Intell. 63: 69-84 (2017) - [j162]Anis Yazidi, B. John Oommen:
A novel technique for stochastic root-finding: Enhancing the search with adaptive d-ary search. Inf. Sci. 393: 108-129 (2017) - [j161]Hugo Lewi Hammer, Anis Yazidi, B. John Oommen:
"Anti-Bayesian" flat and hierarchical clustering using symmetric quantiloids. Inf. Sci. 418: 495-512 (2017) - [j160]Xuan Zhang, B. John Oommen, Ole-Christoffer Granmo:
The design of absorbing Bayesian pursuit algorithms and the formal analyses of their ε-optimality. Pattern Anal. Appl. 20(3): 797-808 (2017) - [j159]Anis Yazidi, B. John Oommen, Morten Goodwin:
On Solving the Problem of Identifying Unreliable Sensors Without a Knowledge of the Ground Truth: The Case of Stochastic Environments. IEEE Trans. Cybern. 47(7): 1604-1617 (2017) - [c177]Anis Yazidi, Hugo Lewi Hammer, B. John Oommen:
A Higher-Fidelity Frugal Quantile Estimator. ADMA 2017: 76-86 - [c176]Anis Yazidi, B. John Oommen, Morten Goodwin:
Identifying Unreliable Sensors Without a Knowledge of the Ground Truth in Deceptive Environments. ADMA 2017: 741-753 - [c175]Hugo Lewi Hammer, Anis Yazidi, B. John Oommen:
On using novel "Anti-Bayesian" techniques for the classification of dynamical data streams. CEC 2017: 1173-1182 - [c174]Thomas McMahon, B. John Oommen:
Enhancing English-Japanese Translation Using Syntactic Pattern Recognition Methods. CORES 2017: 33-42 - [c173]Anis Yazidi, B. John Oommen:
Novel Results on Random Walk-Jump Chains That Possess Tree-Based Transitions. CORES 2017: 43-52 - [c172]Abdolreza Shirvani, B. John Oommen:
Partitioning in signal processing using the object migration automaton and the pursuit paradigm. MLSP 2017: 1-6 - [c171]Rajan Thapa, Lei Jiao, B. John Oommen, Anis Yazidi:
Scheduling Domestic Shiftable Loads in Smart Grids: A Learning Automata-Based Scheme. SmartGIFT 2017: 58-68 - 2016
- [j158]Xuan Zhang, B. John Oommen, Ole-Christoffer Granmo, Lei Jiao:
A formal proof of the 𝜀-optimality of discretized pursuit algorithms. Appl. Intell. 44(2): 282-294 (2016) - [j157]Lei Jiao, Xuan Zhang, B. John Oommen, Ole-Christoffer Granmo:
Optimizing channel selection for cognitive radio networks using a distributed Bayesian learning automata-based approach. Appl. Intell. 44(2): 307-321 (2016) - [j156]Anis Yazidi, B. John Oommen, Geir Horn, Ole-Christoffer Granmo:
Stochastic discretized learning-based weak estimation: a novel estimation method for non-stationary environments. Pattern Recognit. 60: 430-443 (2016) - [j155]Spencer Polk, B. John Oommen:
On Achieving History-Based Move Ordering in Adversarial Board Games Using Adaptive Data Structures. Trans. Comput. Collect. Intell. 22: 10-44 (2016) - [j154]B. John Oommen, Richard Khoury, Aron Schmidt:
Text Classification Using "Anti"-Bayesian Quantile Statistics-Based Classifiers. Trans. Comput. Collect. Intell. 25: 101-126 (2016) - [j153]Anis Yazidi, B. John Oommen:
Novel Discretized Weak Estimators Based on the Principles of the Stochastic Search on the Line Problem. IEEE Trans. Cybern. 46(12): 2732-2744 (2016) - [c170]César A. Astudillo, Jorge Poblete, Marina Resta, B. John Oommen:
A Cluster Analysis of Stock Market Data Using Hierarchical SOMs. Australasian Conference on Artificial Intelligence 2016: 101-112 - [c169]César A. Astudillo, Javier I. González, B. John Oommen, Anis Yazidi:
Concept Drift Detection Using Online Histogram-Based Bayesian Classifiers. Australasian Conference on Artificial Intelligence 2016: 175-182 - [c168]B. John Oommen, Sang-Woon Kim:
On the Foundations of Multinomial Sequence Based Estimation. ICCCI (1) 2016: 218-229 - [c167]Ratish Mohan, Anis Yazidi, Boning Feng, B. John Oommen:
Dynamic Ordering of Firewall Rules Using a Novel Swapping Window-based Paradigm. ICCNS 2016: 11-20 - [c166]B. John Oommen, Sang-Woon Kim:
Multinomial Sequence Based Estimation Using Contiguous Subsequences of Length Three. ICIAR 2016: 243-253 - [c165]Anis Yazidi, Hugo Lewi Hammer, B. John Oommen:
"Anti-Bayesian" Flat and Hierarchical Clustering Using Symmetric Quantiloids. IEA/AIE 2016: 56-67 - [c164]Hanane Tavasoli, B. John Oommen, Anis Yazidi:
On the Online Classification of Data Streams Using Weak Estimators. IEA/AIE 2016: 68-79 - [c163]Spencer Polk, B. John Oommen:
Challenging Established Move Ordering Strategies with Adaptive Data Structures. IEA/AIE 2016: 862-872 - [c162]Akaki Jobava, Anis Yazidi, B. John Oommen, Kyrre M. Begnum:
Achieving Intelligent Traffic-Aware Consolidation of Virtual Machines in a Data Center Using Learning Automata. NTMS 2016: 1-5 - 2015
- [j152]Yifeng Li, B. John Oommen, Alioune Ngom, Luis Rueda:
Pattern classification using a new border identification paradigm: The nearest border technique. Neurocomputing 157: 105-117 (2015) - [c161]Nathan Bell, B. John Oommen:
Particle Field Optimization: A New Paradigm for Swarm Intelligence. AAMAS 2015: 257-265 - [c160]Spencer Polk, B. John Oommen:
Space and depth-related enhancements of the history-ADS strategy in game playing. CIG 2015: 322-327 - [c159]Anis Yazidi, B. John Oommen:
Solving Stochastic Root-Finding with adaptive d-ary search. EAIS 2015: 1-8 - [c158]B. John Oommen, Richard Khoury, Aron Schmidt:
Text Classification Using Novel "Anti-Bayesian" Techniques. ICCCI (1) 2015: 1-15 - [c157]Spencer Polk, B. John Oommen:
Enhancing History-Based Move Ordering in Game Playing Using Adaptive Data Structures. ICCCI (1) 2015: 225-235 - [c156]Spencer Polk, B. John Oommen:
Novel AI Strategies for Multi-Player Games at Intermediate Board States. IEA/AIE 2015: 33-42 - [c155]César A. Astudillo, B. John Oommen:
Pattern Recognition using the TTOCONROT. IEA/AIE 2015: 435-444 - [c154]Hugo Lewi Hammer, Anis Yazidi, B. John Oommen:
A Novel Clustering Algorithm Based on a Non-parametric "Anti-Bayesian" Paradigm. IEA/AIE 2015: 536-545 - [c153]Anis Yazidi, B. John Oommen, Morten Goodwin Olsen:
On Distinguishing between Reliable and Unreliable Sensors Without a Knowledge of the Ground Truth. WI-IAT (2) 2015: 104-111 - [i1]César A. Astudillo, B. John Oommen:
Self Organizing Maps Whose Topologies Can Be Learned With Adaptive Binary Search Trees Using Conditional Rotations. CoRR abs/1506.02750 (2015) - 2014
- [j151]Xuan Zhang, Ole-Christoffer Granmo, B. John Oommen, Lei Jiao:
A formal proof of the ε-optimality of absorbing continuous pursuit algorithms using the theory of regular functions. Appl. Intell. 41(3): 974-985 (2014) - [j150]Qin Ke, B. John Oommen:
Logistic Neural Networks: Their chaotic and pattern recognition properties. Neurocomputing 125: 184-194 (2014) - [j149]César A. Astudillo, B. John Oommen:
Topology-oriented self-organizing maps: a survey. Pattern Anal. Appl. 17(2): 223-248 (2014) - [j148]B. John Oommen, Anu Thomas:
"Anti-Bayesian" parametric pattern classification using order statistics criteria for some members of the exponential family. Pattern Recognit. 47(1): 40-55 (2014) - [j147]César A. Astudillo, B. John Oommen:
Self-organizing maps whose topologies can be learned with adaptive binary search trees using conditional rotations. Pattern Recognit. 47(1): 96-113 (2014) - [j146]Anu Thomas, B. John Oommen:
Corrigendum to three papers that deal with "Anti"-Bayesian Pattern Recognition [Pattern Recognition]. Pattern Recognit. 47(6): 2301-2302 (2014) - [j145]Rokhsareh Sakhravi, Masoud T. Omran, B. John Oommen:
On the Existence and Heuristic Computation of the Solution for the Commons Game. Trans. Comput. Collect. Intell. 14: 71-99 (2014) - [j144]Anis Yazidi, Ole-Christoffer Granmo, B. John Oommen, Morten Goodwin Olsen:
A Novel Strategy for Solving the Stochastic Point Location Problem Using a Hierarchical Searching Scheme. IEEE Trans. Cybern. 44(11): 2202-2220 (2014) - [c152]Anis Yazidi, B. John Oommen, Ole-Christoffer Granmo, Morten Goodwin:
On Utilizing Stochastic Non-linear Fractional Bin Packing to Resolve Distributed Web Crawling. CSE 2014: 32-37 - [c151]Lei Jiao, Xuan Zhang, Ole-Christoffer Granmo, B. John Oommen:
A Bayesian Learning Automata-Based Distributed Channel Selection Scheme for Cognitive Radio Networks. IEA/AIE (2) 2014: 48-57 - [c150]Xuan Zhang, B. John Oommen, Ole-Christoffer Granmo, Lei Jiao:
Using the Theory of Regular Functions to Formally Prove the ε-Optimality of Discretized Pursuit Learning Algorithms. IEA/AIE (1) 2014: 379-388 - [c149]Ke Qin, B. John Oommen:
Cryptanalysis of a Cryptographic Algorithm that Utilizes Chaotic Neural Networks. ISCIS 2014: 167-174 - [c148]César A. Astudillo, B. John Oommen:
Fast BMU Search in SOMs Using Random Hyperplane Trees. PRICAI 2014: 39-51 - 2013
- [j143]Xuan Zhang, Ole-Christoffer Granmo, B. John Oommen:
On incorporating the paradigms of discretization and Bayesian estimation to create a new family of pursuit learning automata. Appl. Intell. 39(4): 782-792 (2013) - [j142]Aleksander Stensby, B. John Oommen, Ole-Christoffer Granmo:
The Use of Weak estimators to Achieve Language Detection and Tracking in Multilingual Documents. Int. J. Pattern Recognit. Artif. Intell. 27(4) (2013) - [j141]B. John Oommen, Ebaa Fayyoumi:
On utilizing dependence-based information to enhance micro-aggregation for secure statistical databases. Pattern Anal. Appl. 16(1): 99-116 (2013) - [j140]César A. Astudillo, B. John Oommen:
On achieving semi-supervised pattern recognition by utilizing tree-based SOMs. Pattern Recognit. 46(1): 293-304 (2013) - [j139]Anu Thomas, B. John Oommen:
The fundamental theory of optimal "Anti-Bayesian" parametric pattern classification using order statistics criteria. Pattern Recognit. 46(1): 376-388 (2013) - [j138]Anu Thomas, B. John Oommen:
Order statistics-based parametric classification for multi-dimensional distributions. Pattern Recognit. 46(12): 3472-3482 (2013) - [j137]Ke Qin, B. John Oommen:
Ideal Chaotic Pattern Recognition Is Achievable: The Ideal-M-AdNN - Its Design and Properties. Trans. Comput. Collect. Intell. 11: 22-51 (2013) - [j136]Anis Yazidi, Ole-Christoffer Granmo, B. John Oommen:
Learning-Automaton-Based Online Discovery and Tracking of Spatiotemporal Event Patterns. IEEE Trans. Cybern. 43(3): 1118-1130 (2013) - [j135]B. John Oommen, M. Khaled Hashem:
Modeling the "Learning Process" of the Teacher in a Tutorial-Like System Using Learning Automata. IEEE Trans. Cybern. 43(6): 2020-2031 (2013) - [c147]Anu Thomas, B. John Oommen:
Ultimate Order Statistics-Based Prototype Reduction Schemes. Australasian Conference on Artificial Intelligence 2013: 421-433 - [c146]Yifeng Li, B. John Oommen, Alioune Ngom, Luis Rueda:
A New Paradigm for Pattern Classification: Nearest Border Techniques. Australasian Conference on Artificial Intelligence 2013: 441-446 - [c145]Anu Thomas, B. John Oommen:
A Novel Border Identification Algorithm Based on an "Anti-Bayesian" Paradigm. CAIP (1) 2013: 196-203 - [c144]Anu Thomas, B. John Oommen:
On Achieving Near-Optimal "Anti-Bayesian" Order Statistics-Based Classification for Asymmetric Exponential Distributions. CAIP (1) 2013: 368-376 - [c143]Anu Thomas, B. John Oommen:
Classification of Multi-dimensional Distributions Using Order Statistics Criteria. CORES 2013: 19-29 - [c142]Xuan Zhang, Ole-Christoffer Granmo, B. John Oommen, Lei Jiao:
On Using the Theory of Regular Functions to Prove the ε-Optimality of the Continuous Pursuit Learning Automaton. IEA/AIE 2013: 262-271 - [c141]Xuan Zhang, Lei Jiao, Ole-Christoffer Granmo, B. John Oommen:
Channel selection in Cognitive Radio Networks: A Switchable Bayesian Learning Automata approach. PIMRC 2013: 2362-2367 - [c140]Spencer Polk, B. John Oommen:
On Applying Adaptive Data Structures to Multi-Player Game Playing. SGAI Conf. 2013: 125-138 - [c139]Spencer Polk, B. John Oommen:
On Enhancing Recent Multi-player Game Playing Strategies Using a Spectrum of Adaptive Data Structures. TAAI 2013: 164-169 - 2012
- [j134]Anis Yazidi, Ole-Christoffer Granmo, B. John Oommen:
Service selection in stochastic environments: a learning-automaton based solution. Appl. Intell. 36(3): 617-637 (2012) - [j133]Ke Qin, B. John Oommen:
The entire range of Chaotic pattern recognition properties possessed by the Adachi neural network. Intell. Decis. Technol. 6(1): 27-41 (2012) - [j132]B. John Oommen, Anis Yazidi, Ole-Christoffer Granmo:
An Adaptive Approach to Learning the Preferences of Users in a Social Network Using Weak Estimators. J. Inf. Process. Syst. 8(2): 191-212 (2012) - [j131]Sang-Woon Kim, B. John Oommen:
On using prototype reduction schemes to optimize locally linear reconstruction methods. Pattern Recognit. 45(1): 498-511 (2012) - [j130]Dragos Calitoiu, B. John Oommen, Doron Nussbaum:
Large-scale neuro-modeling for understanding and explaining some brain-related chaotic behavior. Simul. 88(11): 1316-1337 (2012) - [j129]Colin Bellinger, B. John Oommen:
On the Pattern Recognition and Classification of Stochastically Episodic Events. Trans. Comput. Collect. Intell. 6: 1-35 (2012) - [j128]B. John Oommen, M. Khaled Hashem:
Modeling a Teacher in a Tutorial-like System Using Learning Automata. Trans. Comput. Collect. Intell. 8: 37-62 (2012) - [c138]Anu Thomas, B. John Oommen:
Optimal "Anti-Bayesian" Parametric Pattern Classification Using Order Statistics Criteria. CIARP 2012: 1-13 - [c137]Rokhsareh Sakhravi, Masoud T. Omran, B. John Oommen:
A Fast Heuristic Solution for the Commons Game. DCAI 2012: 81-90 - [c136]Anis Yazidi, B. John Oommen, Ole-Christoffer Granmo:
A novel Stochastic Discretized Weak Estimator operating in non-stationary environments. ICNC 2012: 364-370 - [c135]Anu Thomas, B. John Oommen:
Optimal "Anti-Bayesian" Parametric Pattern Classification for the Exponential Family Using Order Statistics Criteria. ICIAR (1) 2012: 11-18 - [c134]Anis Yazidi, Ole-Christoffer Granmo, B. John Oommen:
A Stochastic Search on the Line-Based Solution to Discretized Estimation. IEA/AIE 2012: 764-773 - [c133]Anis Yazidi, Ole-Christoffer Granmo, B. John Oommen, Morten Goodwin Olsen:
A Hierarchical Learning Scheme for Solving the Stochastic Point Location Problem. IEA/AIE 2012: 774-783 - [c132]Xuan Zhang, Ole-Christoffer Granmo, B. John Oommen:
Discretized Bayesian Pursuit - A New Scheme for Reinforcement Learning. IEA/AIE 2012: 784-793 - 2011
- [j127]Ole-Christoffer Granmo, B. John Oommen:
Learning automata-based solutions to the optimal web polling problem modelled as a nonlinear fractional knapsack problem. Eng. Appl. Artif. Intell. 24(7): 1238-1251 (2011) - [j126]César A. Astudillo, B. John Oommen:
Imposing tree-based topologies onto self organizing maps. Inf. Sci. 181(18): 3798-3815 (2011) - [j125]Justin Zhan, B. John Oommen, Johanna Crisostomo:
Anomaly Detection in Dynamic Systems Using Weak Estimators. ACM Trans. Internet Techn. 11(1): 3:1-3:16 (2011) - [j124]Anis Yazidi, Ole-Christoffer Granmo, B. John Oommen, Martin Gerdes, Frank Reichert:
A User-Centric Approach for Personalized Service Provisioning in Pervasive Environments. Wirel. Pers. Commun. 61(3): 543-566 (2011) - [c131]Colin Bellinger, B. John Oommen:
A New Frontier in Novelty Detection: Pattern Recognition of Stochastically Episodic Events. ACIIDS (1) 2011: 435-444 - [c130]César A. Astudillo, B. John Oommen:
Semi-Supervised Classification Using Tree-Based Self-Organizing Maps. Australasian Conference on Artificial Intelligence 2011: 21-30 - [c129]Anis Yazidi, Ole-Christoffer Granmo, B. John Oommen:
Tracking the Preferences of Users Using Weak Estimators. Australasian Conference on Artificial Intelligence 2011: 799-808 - [c128]Anis Yazidi, Ole-Christoffer Granmo, B. John Oommen:
On the analysis of a new Markov chain which has applications in AI and machine learning. CCECE 2011: 1553-1558 - [c127]Anis Yazidi, Ole-Christoffer Granmo, B. John Oommen:
A New Tool for the Modeling of AI and Machine Learning Applications: Random Walk-Jump Processes. HAIS (1) 2011: 11-21 - [c126]Petro Verkhogliad, B. John Oommen:
Using Artificial Intelligence Techniques for Strategy Generation in the Commons Game. HAIS (1) 2011: 43-50 - [c125]Xuan Zhang, Ole-Christoffer Granmo, B. John Oommen:
The Bayesian Pursuit Algorithm: A New Family of Estimator Learning Automata. IEA/AIE (2) 2011: 522-531 - [c124]Xuan Zhang, B. John Oommen, Ole-Christoffer Granmo:
Generalized Bayesian Pursuit: A Novel Scheme for Multi-Armed Bernoulli Bandit Problems. EANN/AIAI (2) 2011: 122-131 - [c123]Justin Zhan, B. John Oommen, Johanna Crisostomo:
Anomaly detection using weak estimators. ISI 2011: 143-149 - [c122]B. John Oommen:
On Merging the Fields of Neural Networks and Adaptive Data Structures to Yield New Pattern Recognition Methodologies. PReMI 2011: 13-16 - 2010
- [j123]B. John Oommen, M. Khaled Hashem:
Modeling a Domain in a Tutorial-like System Using Learning Automata. Acta Cybern. 19(3): 635-653 (2010) - [j122]Ole-Christoffer Granmo, B. John Oommen:
Optimal sampling for estimation with constrained resources using a learning automaton-based solution for the nonlinear fractional knapsack problem. Appl. Intell. 33(1): 3-20 (2010) - [j121]Luis Rueda, B. John Oommen, Claudio Henríquez:
Multi-class pairwise linear dimensionality reduction using heteroscedastic schemes. Pattern Recognit. 43(7): 2456-2465 (2010) - [j120]Eser Aygün, B. John Oommen, Zehra Cataltepe:
Peptide classification using optimal and information theoretic syntactic modeling. Pattern Recognit. 43(11): 3891-3899 (2010) - [j119]Ebaa Fayyoumi, B. John Oommen:
A survey on statistical disclosure control and micro-aggregation techniques for secure statistical databases. Softw. Pract. Exp. 40(12): 1161-1188 (2010) - [j118]Ole-Christoffer Granmo, B. John Oommen:
Solving Stochastic Nonlinear Resource Allocation Problems Using a Hierarchy of Twofold Resource Allocation Automata. IEEE Trans. Computers 59(4): 545-560 (2010) - [j117]B. John Oommen, Sudip Misra:
Fault-tolerant routing in adversarial mobile ad hoc networks: an efficient route estimation scheme for non-stationary environments. Telecommun. Syst. 44(1-2): 159-169 (2010) - [j116]Geir Horn, B. John Oommen:
Solving Multiconstraint Assignment Problems Using Learning Automata. IEEE Trans. Syst. Man Cybern. Part B 40(1): 6-18 (2010) - [j115]B. John Oommen, M. Khaled Hashem:
Modeling a Student-Classroom Interaction in a Tutorial-Like System Using Learning Automata. IEEE Trans. Syst. Man Cybern. Part B 40(1): 29-42 (2010) - [j114]Sudip Misra, B. John Oommen, Sreekeerthy Yanamandra, Mohammad S. Obaidat:
Random Early Detection for Congestion Avoidance in Wired Networks: A Discretized Pursuit Learning-Automata-Like Solution. IEEE Trans. Syst. Man Cybern. Part B 40(1): 66-76 (2010) - [j113]B. John Oommen, Ebaa Fayyoumi:
On Utilizing Association and Interaction Concepts for Enhancing Microaggregation in Secure Statistical Databases. IEEE Trans. Syst. Man Cybern. Part B 40(1): 198-207 (2010) - [j112]B. John Oommen, M. Khaled Hashem:
Modeling a Student's Behavior in a Tutorial-Like System Using Learning Automata. IEEE Trans. Syst. Man Cybern. Part B 40(2): 481-492 (2010) - [c121]Petro Verkhogliad, B. John Oommen:
Potential AI Strategies to Solve the Commons Game: A Position Paper. Canadian AI 2010: 352-356 - [c120]Sang-Woon Kim, B. John Oommen:
On Optimizing Locally Linear Nearest Neighbour Reconstructions Using Prototype Reduction Schemes. Australasian Conference on Artificial Intelligence 2010: 153-163 - [c119]Thomas Norheim, Terje Brådland, Ole-Christoffer Granmo, B. John Oommen:
A Generic Solution to Multi-Armed Bernoulli Bandit Problems based on Random Sampling from Sibling Conjugate Priors. ICAART (1) 2010: 36-44 - [c118]Dragos Calitoiu, B. John Oommen:
On using Simulation and Stochastic Learning for Pattern Recognition When Training Data is Unavailable - The Case of Disease Outbreak. ICAART (1) 2010: 45-52 - [c117]Anis Yazidi, Ole-Christoffer Granmo, B. John Oommen:
A Learning Automata Based Solution to Service Selection in Stochastic Environments. IEA/AIE (3) 2010: 209-218 - [c116]Anis Yazidi, Ole-Christoffer Granmo, Min Lin, Xifeng Wen, B. John Oommen, Martin Gerdes, Frank Reichert:
Learning Automaton Based On-Line Discovery and Tracking of Spatio-temporal Event Patterns. PRICAI 2010: 327-338 - [c115]Colin Bellinger, B. John Oommen:
On simulating episodic events against a background of noise-like non-episodic events. SummerSim 2010: 452-460 - [c114]Aleksander Stensby, B. John Oommen, Ole-Christoffer Granmo:
Language Detection and Tracking in Multilingual Documents Using Weak Estimators. SSPR/SPR 2010: 600-609
2000 – 2009
- 2009
- [j111]Sudip Misra, B. John Oommen:
An efficient pursuit automata approach for estimating stable all-pairs shortest paths in stochastic network environments. Int. J. Commun. Syst. 22(4): 441-468 (2009) - [j110]Qingxin Zhu, B. John Oommen:
Estimation of distributions involving unobservable events: the case of optimal search with unknown Target Distributions. Pattern Anal. Appl. 12(1): 37-53 (2009) - [j109]Sang-Woon Kim, B. John Oommen:
On using prototype reduction schemes to enhance the computation of volume-based inter-class overlap measures. Pattern Recognit. 42(11): 2695-2704 (2009) - [j108]Ke Qin, B. John Oommen:
Adachi-Like Chaotic Neural Networks Requiring Linear-Time Computations by Enforcing a Tree-Shaped Topology. IEEE Trans. Neural Networks 20(11): 1797-1809 (2009) - [j107]Ebaa Fayyoumi, B. John Oommen:
Achieving Microaggregation for Secure Statistical Databases Using Fixed-Structure Partitioning-Based Learning Automata. IEEE Trans. Syst. Man Cybern. Part B 39(5): 1192-1205 (2009) - [c113]Sudip Misra, B. John Oommen, Sreekeerthy Yanamandra, Mohammad S. Obaidat:
An adaptive learning-like solution of random early detection for congestion avoidance in computer networks. AICCSA 2009: 485-491 - [c112]César A. Astudillo, B. John Oommen:
On Using Adaptive Binary Search Trees to Enhance Self Organizing Maps. Australasian Conference on Artificial Intelligence 2009: 199-209 - [c111]Justin Zhan, B. John Oommen, Johanna Crisostomo:
Anomaly Detection in Dynamic Social Systems Using Weak Estimators. CSE (4) 2009: 18-25 - [c110]Ole-Christoffer Granmo, B. John Oommen:
A Hierarchy of Twofold Resource Allocation Automata Supporting Optimal Sampling. IEA/AIE 2009: 523-534 - [c109]B. John Oommen, M. Khaled Hashem:
Learning Automata Based Intelligent Tutorial-like System. KES (1) 2009: 360-373 - [c108]Eser Aygün, B. John Oommen, Zehra Cataltepe:
On Utilizing Optimal and Information Theoretic Syntactic Modeling for Peptide Classification. PRIB 2009: 24-35 - [p4]B. John Oommen, Sudip Misra:
Cybernetics and Learning Automata. Handbook of Automation 2009: 221-235 - [p3]César A. Astudillo, B. John Oommen:
A Novel Self Organizing Map Which Utilizes Imposed Tree-Based Topologies. Computer Recognition Systems 3 2009: 169-178 - [p2]Ole-Christoffer Granmo, B. John Oommen:
Learning Automata-Based Solutions to Stochastic Nonlinear Resource Allocation Problems. Intelligent Systems for Knowledge Management 2009: 1-30 - 2008
- [j106]Dragos Calitoiu, B. John Oommen, Doron Nussbaum:
Spikes annihilation in the Hodgkin-Huxley neuron. Biol. Cybern. 98(3): 239-257 (2008) - [j105]Luis Rueda, B. John Oommen:
An efficient compression scheme for data communication which uses a new family of self-organizing binary search trees. Int. J. Commun. Syst. 21(10): 1091-1120 (2008) - [j104]B. John Oommen, Sang-Woon Kim, M. T. Samuel, Ole-Christoffer Granmo:
A Solution to the Stochastic Point Location Problem in Metalevel Nonstationary Environments. IEEE Trans. Syst. Man Cybern. Part B 38(2): 466-476 (2008) - [j103]Sang-Woon Kim, B. John Oommen:
On Using Prototype Reduction Schemes to Optimize Kernel-Based Fisher Discriminant Analysis. IEEE Trans. Syst. Man Cybern. Part B 38(2): 564-570 (2008) - [c107]B. John Oommen, Ebaa Fayyoumi:
Enhancing Micro-Aggregation Technique by Utilizing Dependence-Based Information in Secure Statistical Databases. ACISP 2008: 404-418 - [c106]Sang-Woon Kim, B. John Oommen:
A Fast Computation of Inter-class Overlap Measures Using Prototype Reduction Schemes. Canadian AI 2008: 173-184 - [c105]B. John Oommen, Ebaa Fayyoumi:
An AI-Based Causal Strategy for Securing Statistical Databases Using Micro-aggregation. Australasian Conference on Artificial Intelligence 2008: 423-434 - [c104]Luis Rueda, Claudio Henríquez, B. John Oommen:
Chernoff-Based Multi-class Pairwise Linear Dimensionality Reduction. CIARP 2008: 301-308 - [c103]Ole-Christoffer Granmo, B. John Oommen:
A Hierarchy of Twofold Resource Allocation Automata Supporting Optimal Web Polling. IEA/AIE 2008: 347-358 - [c102]B. John Oommen, Dragos Calitoiu:
Modeling and simulating a disease outbreak by learning a contagion parameter-based model. SpringSim 2008: 547-555 - [c101]Dragos Calitoiu, Doron Nussbaum, B. John Oommen:
Large scale modeling of the piriform cortex for analyzing antiepileptic effects. SpringSim 2008: 599-608 - [c100]Ke Qin, B. John Oommen:
Chaotic Pattern Recognition: The Spectrum of Properties of the Adachi Neural Network. SSPR/SPR 2008: 540-550 - 2007
- [j102]Abdelrahman Amer, B. John Oommen:
A Novel Framework for Self-Organizing Lists in Environments with Locality of Reference: Lists-on-Lists. Comput. J. 50(2): 186-196 (2007) - [j101]B. John Oommen, Ghada Hany Badr:
Breadth-first search strategies for trie-based syntactic pattern recognition. Pattern Anal. Appl. 10(1): 1-13 (2007) - [j100]Dragos Calitoiu, B. John Oommen, Doron Nussbaum:
Periodicity and stability issues of a chaotic pattern recognition neural network. Pattern Anal. Appl. 10(3): 175-188 (2007) - [j99]Sang-Woon Kim, B. John Oommen:
On using prototype reduction schemes to optimize dissimilarity-based classification. Pattern Recognit. 40(11): 2946-2957 (2007) - [j98]B. John Oommen, Sang-Woon Kim, Geir Horn:
On the estimation of independent binomial random variables using occurrence and sequential information. Pattern Recognit. 40(11): 3263-3276 (2007) - [j97]B. John Oommen, Sudip Misra, Ole-Christoffer Granmo:
Routing Bandwidth-Guaranteed Paths in MPLS Traffic Engineering: A Multiple Race Track Learning Approach. IEEE Trans. Computers 56(7): 959-976 (2007) - [j96]Pradeep K. Atrey, Mohan S. Kankanhalli, B. John Oommen:
Goal-oriented optimal subset selection of correlated multimedia streams. ACM Trans. Multim. Comput. Commun. Appl. 3(1): 2 (2007) - [j95]Ole-Christoffer Granmo, B. John Oommen, Svein Arild Myrer, Morten Goodwin Olsen:
Learning Automata-Based Solutions to the Nonlinear Fractional Knapsack Problem With Applications to Optimal Resource Allocation. IEEE Trans. Syst. Man Cybern. Part B 37(1): 166-175 (2007) - [j94]Dragos Calitoiu, B. John Oommen, Doron Nussbaum:
Desynchronizing a Chaotic Pattern Recognition Neural Network to Model Inaccurate Perception. IEEE Trans. Syst. Man Cybern. Part B 37(3): 692-704 (2007) - [c99]Dragos Calitoiu, B. John Oommen, Doron Nussbaum:
Analytic Results on the Hodgkin-Huxley Neural Network: Spikes Annihilation. Canadian AI 2007: 320-331 - [c98]Sudip Misra, B. John Oommen:
The Pursuit Automaton Approach for Estimating All-Pairs Shortest Paths in Dynamically Changing Networks. AINA Workshops (1) 2007: 124-129 - [c97]Ole-Christoffer Granmo, B. John Oommen:
On Using a Hierarchy of Twofold Resource Allocation Automata to Solve Stochastic Nonlinear Resource Allocation Problems. Australian Conference on Artificial Intelligence 2007: 36-47 - [c96]Dragos Calitoiu, B. John Oommen, Doron Nussbaum:
Some Analysis on the Network of Bursting Neurons: Quantifying Behavioral Synchronization. Australian Conference on Artificial Intelligence 2007: 110-119 - [c95]Dragos Calitoiu, B. John Oommen, Doron Nussbaum:
Numerical Results on the Hodgkin-Huxley Neural Network: Spikes Annihilation. BVAI 2007: 378-387 - [c94]B. John Oommen, Ole-Christoffer Granmo, Asle Pedersen:
Using Stochastic AI Techniques to Achieve Unbounded Resolution in Finite Player Goore Games and its Applications. CIG 2007: 161-167 - [c93]B. John Oommen, Ebaa Fayyoumi:
A Novel Method for Micro-Aggregation in Secure Statistical Databases Using Association and Interaction. ICICS 2007: 126-140 - [c92]M. Khaled Hashem, B. John Oommen:
On Using Learning Automata to Model a Student's Behavior in a Tutorial-like System. IEA/AIE 2007: 813-822 - [c91]B. John Oommen, Sang-Woon Kim, Mathew Samuel, Ole-Christoffer Granmo:
Stochastic Point Location in Non-stationary Environments and Its Applications. IEA/AIE 2007: 845-854 - [c90]M. Khaled Hashem, B. John Oommen:
Using learning automata to model the behavior of a teacher in a tutorial-like system. SMC 2007: 76-82 - [c89]M. Khaled Hashem, B. John Oommen:
Using learning automata to model a student-classroom interaction in a tutorial-like system. SMC 2007: 1177-1182 - 2006
- [j93]Luis Rueda, B. John Oommen:
A fast and efficient nearly-optimal adaptive Fano coding scheme. Inf. Sci. 176(12): 1656-1683 (2006) - [j92]Ghada Badr, B. John Oommen:
A novel look-ahead optimization strategy for trie-based approximate string matching. Pattern Anal. Appl. 9(2-3): 177-187 (2006) - [j91]Sang-Woon Kim, B. John Oommen:
Prototype reduction schemes applicable for non-stationary data sets. Pattern Recognit. 39(2): 209-222 (2006) - [j90]B. John Oommen, Luis Rueda:
Stochastic learning-based weak estimation of multinomial random variables and its applications to pattern recognition in non-stationary environments. Pattern Recognit. 39(3): 328-341 (2006) - [j89]Sudip Misra, B. John Oommen:
An Efficient Dynamic Algorithm for Maintaining All-Pairs Shortest Paths in Stochastic Networks. IEEE Trans. Computers 55(6): 686-702 (2006) - [j88]Ghada Badr, B. John Oommen:
On optimizing syntactic pattern recognition using tries and AI-based heuristic-search strategies. IEEE Trans. Syst. Man Cybern. Part B 36(3): 611-622 (2006) - [j87]B. John Oommen, Govindachari Raghunath, Benjamin Kuipers:
Parameter learning from stochastic teachers and stochastic compulsive liars. IEEE Trans. Syst. Man Cybern. Part B 36(4): 820-834 (2006) - [j86]Luis Rueda, B. John Oommen:
Stochastic Automata-Based Estimators for Adaptively Compressing Files With Nonstationary Distributions. IEEE Trans. Syst. Man Cybern. Part B 36(5): 1196-1200 (2006) - [c88]Xavier Hilaire, B. John Oommen:
The averaged mappings problem: statement, applications, and approximate solution. ACM Southeast Regional Conference 2006: 24-29 - [c87]Ebaa Fayyoumi, B. John Oommen:
On Optimizing the k-Ward Micro-aggregation Technique for Secure Statistical Databases. ACISP 2006: 324-335 - [c86]Denis V. Batalov, B. John Oommen:
Turning Lights Out with DQ-Learning. Artificial Intelligence and Applications 2006: 451-456 - [c85]B. John Oommen, Ole-Christoffer Granmo, Asle Pedersen:
Empirical Verification of a Strategy for Unbounded Resolution in Finite Player Goore Games. Australian Conference on Artificial Intelligence 2006: 1252-1258 - [c84]B. John Oommen, Jing Chen:
On Utilizing Attribute Cardinality Maps to Enhance Query Optimization in the Oracle Database System. ICEIS (1) 2006: 23-35 - [c83]B. John Oommen, Jing Chen:
On Enhancing Query Optimization in the Oracle Database System by Utilizing Attribute Cardinality Maps. ICEIS (Selected Papers) 2006: 38-71 - [c82]Sang-Woon Kim, B. John Oommen:
On Optimizing Dissimilarity-Based Classification Using Prototype Reduction Schemes. ICIAR (1) 2006: 15-28 - [c81]Ole-Christoffer Granmo, B. John Oommen:
On Allocating Limited Sampling Resources Using a Learning Automata-based Solution to the Fractional Knapsack Problem. Intelligent Information Systems 2006: 263-272 - [c80]B. John Oommen, Sudip Misra, Ole-Christoffer Granmo:
A Stochastic Random-Races Algorithm for Routing in MPLS Traffic Engineering. INFOCOM 2006 - [c79]Ebaa Fayyoumi, B. John Oommen:
A Fixed Structure Learning Automaton Micro-aggregation Technique for Secure Statistical Databases. Privacy in Statistical Databases 2006: 114-128 - [c78]Geir Horn, B. John Oommen:
An Application of a Game of Discrete Generalised Pursuit Automata to Solve a Multi-Constraint Partitioning Problem. SMC 2006: 1042-1049 - [c77]B. John Oommen, Sang-Woon Kim, Geir Horn:
On the Theory and Applications of Sequence Based Estimation of Independent Binomial Random Variables. SSPR/SPR 2006: 8-21 - [c76]Sang-Woon Kim, B. John Oommen:
On Optimizing Kernel-Based Fisher Discriminant Analysis Using Prototype Reduction Schemes. SSPR/SPR 2006: 826-834 - [c75]Abdelrahman Amer, B. John Oommen:
Lists on Lists: A Framework for Self-organizing Lists in Environments with Locality of Reference. WEA 2006: 109-120 - [c74]B. John Oommen, Sudip Misra:
A Fault-Tolerant Routing Algorithm for Mobile Ad Hoc Networks Using a Stochastic Learning-Based Weak Estimation Procedure. WiMob 2006: 31-37 - 2005
- [j85]B. John Oommen, Luís G. Rueda:
A formal analysis of why heuristic functions work. Artif. Intell. 164(1-2): 1-22 (2005) - [j84]Ghada Hany Badr, B. John Oommen:
Self-Adjusting of Ternary Search Tries Using Conditional Rotations and Randomized Heuristics. Comput. J. 48(2): 200-219 (2005) - [j83]Sang-Woon Kim, B. John Oommen:
On Utilizing Search Methods to Select Subspace Dimensions for Kernel-Based Nonlinear Subspace Classifiers. IEEE Trans. Pattern Anal. Mach. Intell. 27(1): 136-141 (2005) - [j82]Sang-Woon Kim, B. John Oommen:
On Using Prototype Reduction Schemes and Classifier Fusion Strategies to Optimize Kernel-Based Nonlinear Subspace Methods. IEEE Trans. Pattern Anal. Mach. Intell. 27(3): 455-460 (2005) - [j81]Sudip Misra, B. John Oommen:
Dynamic algorithms for the shortest path routing problem: learning automata-based solutions. IEEE Trans. Syst. Man Cybern. Part B 35(6): 1179-1192 (2005) - [c73]Ghada Badr, B. John Oommen:
On using conditional rotations and randomized heuristics for self-organizing ternary search tries. ACM Southeast Regional Conference (1) 2005: 109-115 - [c72]Sang-Woon Kim, B. John Oommen:
Time-Varying Prototype Reduction Schemes Applicable for Non-stationary Data Sets. Australian Conference on Artificial Intelligence 2005: 614-623 - [c71]Dragos Calitoiu, B. John Oommen, Doron Nussbaum:
Neural Network-Based Chaotic Pattern Recognition - Part 2: Stability and Algorithmic Issues. CORES 2005: 3-16 - [c70]Ghada Badr, B. John Oommen:
A Look-Ahead Branch and Bound Pruning Scheme for Trie-Based Approximate String Matching. CORES 2005: 87-94 - [c69]Ghada Badr, B. John Oommen:
Enhancing Trie-Based Syntactic Pattern Recognition Using AI Heuristic Search Strategies. ICAPR (1) 2005: 1-17 - [c68]Geir Horn, B. John Oommen:
A Fixed-Structure Learning Automaton Solution to the Stochastic Static Mapping Problem. IPDPS 2005 - [c67]Sudip Misra, B. John Oommen:
New Algorithms for Maintaining All-Pairs Shortest Paths. ISCC 2005: 116-121 - [c66]Luís G. Rueda, B. John Oommen:
Efficient Adaptive Data Compression Using Fano Binary Search Trees. ISCIS 2005: 768-779 - [c65]B. John Oommen, Luís G. Rueda:
On Utilizing Stochastic Learning Weak Estimators for Training and Classification of Patterns with Non-stationary Distributions. KI 2005: 107-120 - [c64]Dragos Calitoiu, B. John Oommen, Dorin Nusbaumm:
Modeling Inaccurate Perception: Desynchronization Issues of a Chaotic Pattern Recognition Neural Network. SCIA 2005: 821-830 - 2004
- [j80]Sudip Misra, B. John Oommen:
GPSPA: a new adaptive algorithm for maintaining shortest path routing trees in stochastic networks. Int. J. Commun. Syst. 17(10): 963-984 (2004) - [j79]Luís G. Rueda, B. John Oommen:
A nearly-optimal Fano-based coding algorithm. Inf. Process. Manag. 40(2): 257-268 (2004) - [j78]M. Ouerd, B. John Oommen, Stan Matwin:
A formal approach to using data distributions for building causal polytree structures. Inf. Sci. 168(1-4): 111-132 (2004) - [j77]Sang-Woon Kim, B. John Oommen:
On using prototype reduction schemes to optimize kernel-based nonlinear subspace methods. Pattern Recognit. 37(2): 227-239 (2004) - [j76]Sang-Woon Kim, B. John Oommen:
Enhancing prototype reduction schemes with recursion: a method applicable for "large" data sets. IEEE Trans. Syst. Man Cybern. Part B 34(3): 1384-1397 (2004) - [c63]B. John Oommen, Jack R. Zgierski, Doron Nussbaum:
Deterministic Majority filters applied to stochastic sorting. ACM Southeast Regional Conference 2004: 228-233 - [c62]Sudip Misra, B. John Oommen:
Adaptive Algorithms for Routing and Traffic Engineering in Stochastic Networks. AAAI 2004: 993-994 - [c61]Luís G. Rueda, B. John Oommen:
On Families of New Adaptive Compression Algorithms Suitable for Time-Varying Source Data. ADVIS 2004: 234-244 - [c60]Sang-Woon Kim, B. John Oommen:
Selecting Subspace Dimensions for Kernel-Based Nonlinear Subspace Classifiers Using Intelligent Search Methods. Australian Conference on Artificial Intelligence 2004: 1115-1121 - [c59]Sudip Misra, B. John Oommen:
Stochastic Learning Automata-Based Dynamic Algorithms for the Single Source Shortest Path Problem. IEA/AIE 2004: 239-248 - [c58]Sudip Misra, B. John Oommen:
Generalized pursuit learning algorithms for shortest path routing tree computation. ISCC 2004: 891-896 - [c57]B. John Oommen, Jack R. Zgierski, Doron Nussbaum:
Stochastic Sorting Using Deterministic Consecutive and Leader Filters. MSV/AMCS 2004: 399-405 - [c56]B. John Oommen:
Recent Results on Learning from Stochastic Teachers and Compulsive Liars/Con-Men. PRIS 2004: 4 - [c55]Qun Wang, B. John Oommen:
On Designing Pattern Classifiers Using Artificially Created Bootstrap Samples. PRIS 2004: 159-168 - [c54]B. John Oommen, Ghada Badr:
Dictionary-Based Syntactic Pattern Recognition Using Tries. SSPR/SPR 2004: 251-259 - [c53]B. John Oommen, Luís G. Rueda:
A New Family of Weak Estimators for Training in Non-stationary Distributions. SSPR/SPR 2004: 644-652 - 2003
- [j75]Sang-Woon Kim, B. John Oommen:
A brief taxonomy and ranking of creative prototype reduction schemes. Pattern Anal. Appl. 6(3): 232-244 (2003) - [j74]Luís G. Rueda, B. John Oommen:
On optimal pairwise linear classifiers for normal distributions: the d-dimensional case. Pattern Recognit. 36(1): 13-23 (2003) - [j73]Sang-Woon Kim, B. John Oommen:
Enhancing prototype reduction schemes with LVQ3-type algorithms. Pattern Recognit. 36(5): 1083-1093 (2003) - [j72]Necati Aras, I. Kuban Altinel, B. John Oommen:
A Kohonen-like decomposition method for the Euclidean traveling salesman problem-KNIES_DECOMPOSE. IEEE Trans. Neural Networks 14(4): 869-890 (2003) - [j71]B. John Oommen, Murali Thiyagarajah:
Benchmarking attribute cardinality maps for database systems using the TPC-D specifications. IEEE Trans. Syst. Man Cybern. Part B 33(6): 913-924 (2003) - [c52]Ouerd Messaouda, B. John Oommen, Stan Matwin:
Enhancing Caching in Distributed Databases Using Intelligent Polytree Representations. AI 2003: 498-504 - [c51]B. John Oommen, Govindachari Raghunath, Benjamin Kuipers:
On How to Learn from a Stochastic Teacher or a Stochastic Compulsive Liar of Unknown Identity. Australian Conference on Artificial Intelligence 2003: 24-40 - [c50]Sang-Woon Kim, B. John Oommen:
On Using Prototype Reduction Schemes and Classifier Fusion Strategies to Optimize Kernel-Based Nonlinear Subspace Methods. Australian Conference on Artificial Intelligence 2003: 783-795 - [c49]B. John Oommen, Jing Chen:
A new histogram method for sparse attributes: the averaged rectangular attribute cardinality map. ISICT 2003: 119-125 - [c48]I. Kuban Altinel, Necati Aras, B. John Oommen:
A self-organizing method for map reconstruction. NNSP 2003: 677-687 - [c47]Qun Wang, B. John Oommen:
Classification Error-Rate Estimation Using New Pseudo-Sample Bootstrap Methods. PRIS 2003: 96-103 - 2002
- [j70]B. John Oommen, Luís G. Rueda:
The Efficiency of Histogram-like Techniques for Database Query Optimization. Comput. J. 45(5): 494-510 (2002) - [j69]Luís G. Rueda, B. John Oommen:
On Optimal Pairwise Linear Classifiers for Normal Distributions: The Two-Dimensional Case. IEEE Trans. Pattern Anal. Mach. Intell. 24(2): 274-280 (2002) - [j68]Gopal Racherla, Sridhar Radhakrishnan, B. John Oommen:
Enhanced layered segment trees: a pragmatic data structure for real-time processing of geometric objects. Pattern Recognit. 35(10): 2303-2309 (2002) - [j67]M. Agache, B. John Oommen:
Generalized pursuit learning schemes: new families of continuous and discretized learning automata. IEEE Trans. Syst. Man Cybern. Part B 32(6): 738-749 (2002) - [j66]B. John Oommen, T. Dale Roberts:
Discretized learning automata solutions to the capacity assignment problem for prioritized networks. IEEE Trans. Syst. Man Cybern. Part B 32(6): 821-831 (2002) - [c46]Sang-Woon Kim, B. John Oommen:
Optimizing Kernel-Based Nonlinear Subspace Methods Using Prototype Reduction Schemes. Australian Joint Conference on Artificial Intelligence 2002: 155-166 - [c45]B. John Oommen, Luís G. Rueda:
Using Pattern Recognition Techniques to Derive a Formal Analysis of Why Heuristics Functions Work. PRIS 2002: 45-58 - [c44]Sang-Woon Kim, B. John Oommen:
On Utilizing LVQ3-Type Algorithms to Enhance Prototype Reduction Schemes. PRIS 2002: 242-256 - [c43]Sang-Woon Kim, B. John Oommen:
Creative prototype reduction schemes: a taxonomy and ranking. SMC (2) 2002: 6 - [c42]Ouerd Messaouda, B. John Oommen, Stan Matwin:
Data generation for testing DAG-structured Bayesian networks. SMC 2002: 6 - [c41]Sang-Woon Kim, B. John Oommen:
Recursive Prototype Reduction Schemes Applicable for Large Data Sets. SSPR/SPR 2002: 528-537 - 2001
- [j65]B. John Oommen, R. K. S. Loke:
On the Pattern Recognition of Noisy Subsequence Trees. IEEE Trans. Pattern Anal. Mach. Intell. 23(9): 929-946 (2001) - [j64]B. John Oommen, M. Agache:
Continuous and discretized pursuit learning schemes: various algorithms and their comparison. IEEE Trans. Syst. Man Cybern. Part B 31(3): 277-287 (2001) - [c40]Luís G. Rueda, B. John Oommen:
Resolving Minsky's Paradox: The d-Dimensional Normal Distribution Case. Australian Joint Conference on Artificial Intelligence 2001: 25-36 - [c39]B. John Oommen, Luís G. Rueda:
Histogram Methods in Query Optimization: The Relation between Accuracy and Optimality. DASFAA 2001: 320-326 - [c38]Gopal Racherla, Sridhar Radhakrishnan, B. John Oommen:
A New Geometric Tool for Pattern Recognition - An Algorithm for Real Time Insertion of Layered Segment Trees. ICAPR 2001: 212-221 - [c37]B. John Oommen, Qun Wang:
Distance Bias Adjustment Bootstrap Estimation for Bhattacharyya Error Bound in Classifiers. PRIS 2001: 103-117 - [c36]Luís G. Rueda, B. John Oommen:
Enhanced static Fano coding. SMC 2001: 2163-2169 - 2000
- [j63]I. Kuban Altinel, Necati Aras, B. John Oommen:
Fast, efficient and accurate solutions to the Hamiltonian path problem using neural approaches. Comput. Oper. Res. 27(5): 461-494 (2000) - [j62]B. John Oommen, T. Dale Roberts:
Continuous Learning Automata Solutions to the Capacity Assignment Problem. IEEE Trans. Computers 49(6): 608-620 (2000) - [c35]Necati Aras, I. Kuban Altinel, B. John Oommen:
A Kohonen-like Decomposition Method for the Traveling Salesman Problem: KNIESDECOMPOSE. ECAI 2000: 261-265 - [c34]B. John Oommen, Luis Rueda:
An Empirical Comparison of Histogram-Like Techniques for Query Optimization. ICEIS 2000: 71-78 - [c33]B. John Oommen, Murali Thiyagarajah:
Query Result Size Estimation Using the Trapezoidal Attribute Cardinality Map. IDEAS 2000: 236-242 - [c32]M. Ouerd, B. John Oommen, Stan Matwin:
A Formalism for Building Causal Polytree Structures Using Data Distributions. ISMIS 2000: 629-637 - [c31]Luis Rueda, B. John Oommen:
The Foundational Theory of Optimal Bayesian Pairwise Linear Classifiers. SSPR/SPR 2000: 581-590
1990 – 1999
- 1999
- [j61]Necati Aras, B. John Oommen, I. Kuban Altinel:
The Kohonen network incorporating explicit statistics and its application to the travelling salesman problem. Neural Networks 12(9): 1273-1284 (1999) - [j60]B. John Oommen, R. K. S. Loke:
Designing syntactic pattern classifiers using vector quantization and parametric string editing. IEEE Trans. Syst. Man Cybern. Part B 29(6): 881-888 (1999) - [c30]Murali Thiyagarajah, B. John Oommen:
Prototype Validation of the Rectangular Attribute Cardinality Map for Query Optimization in Database Systems. BIS 1999: 250-262 - [c29]Murali Thiyagarajah, B. John Oommen:
On Benchmarking Attribute Cardinality Maps for Database Systems Using the TPC-D Specification. DEXA 1999: 292-301 - [c28]Murali Thiyagarajah, B. John Oommen:
Prototype Validation of the Trapezoidal Attribute Cardinality Map for Query Optimization in Database Systems. ICEIS 1999: 156-162 - [c27]B. John Oommen, Murali Thiyagarajah:
Query Result Size Estimation Using a Novel Histogram-like Technique: The Rectangular Attribute Cardinality Map. IDEAS 1999: 3-15 - [c26]B. John Oommen, T. Dale Roberts:
On Solving the Capacity Assignment Problem Using Continous Learning Automata. IEA/AIE 1999: 622-631 - 1998
- [j59]B. John Oommen, Rangasami L. Kashyap:
A formal theory for optimal and information theoretic syntactic pattern recognition. Pattern Recognit. 31(8): 1159-1177 (1998) - [j58]B. John Oommen, I. Kuban Altinel, Necati Aras:
Discrete vector quantization for arbitrary distance function estimation. IEEE Trans. Syst. Man Cybern. Part B 28(4): 496-510 (1998) - [j57]B. John Oommen, Govindachari Raghunath:
Automata learning and intelligent tertiary searching for stochastic point location. IEEE Trans. Syst. Man Cybern. Part B 28(6): 947-954 (1998) - [c25]B. John Oommen, T. Dale Roberts:
A Fast Efficient Solution to the Capacity Assignment Problem Using Discretized Learning Automata. IEA/AIE (Vol. 2) 1998: 56-65 - [c24]B. John Oommen, R. K. S. Loke:
The Noisy Subsequence Tree Recognition Problem. SSPR/SPR 1998: 169-180 - 1997
- [j56]I. Kuban Altinel, B. John Oommen, Necati Aras:
Vector Quantization for Arbitrary Distance Function Estimation. INFORMS J. Comput. 9(4): 439-451 (1997) - [j55]Thai B. Nguyen, B. John Oommen:
Moment-Preserving Piecewise Linear Approximations of Signals and Images. IEEE Trans. Pattern Anal. Mach. Intell. 19(1): 84-91 (1997) - [j54]B. John Oommen, Richard K. S. Loke:
Pattern recognition of strings with substitutions, insertions, deletions and generalized transpositions. Pattern Recognit. 30(5): 789-800 (1997) - [j53]B. John Oommen, Edward V. de St. Croix:
String taxonomy using learning automata. IEEE Trans. Syst. Man Cybern. Part B 27(2): 354-365 (1997) - [j52]B. John Oommen:
Stochastic searching on the line and its applications to parameter learning in nonlinear optimization. IEEE Trans. Syst. Man Cybern. Part B 27(4): 733-739 (1997) - [c23]B. John Oommen, I. Kuban Altmel, Necati Aras:
Arbitrary distance function estimation using discrete vector quantization. ICNN 1997: 1272-1277 - [c22]B. John Oommen, Juan Dong:
Generalized Swap-with-Parent Schemes for Self-Organizing Sequential Linear Lists. ISAAC 1997: 414-423 - [c21]Qingxin Zhu, B. John Oommen:
On the Optimal Search Problem: The Case when the Target Distribution is Unknown. SCCC 1997: 268-277 - 1996
- [j51]B. John Oommen, K. Zhang:
The Normalized String Editing Problem Revisited. IEEE Trans. Pattern Anal. Mach. Intell. 18(6): 669-672 (1996) - [j50]B. John Oommen, Edward V. de St. Croix:
Graph Partitioning Using Learning Automata. IEEE Trans. Computers 45(2): 195-208 (1996) - [j49]B. John Oommen, K. Zhang, William Lee:
Numerical Similarity and Dissimilarity Measures Between Two Trees. IEEE Trans. Computers 45(12): 1426-1434 (1996) - [c20]B. John Oommen, R. K. S. Loke:
Optimal and Information Theoretic Syntactic Pattern Recognition Involving Traditional and Transposition Errors. FSTTCS 1996: 224-237 - [c19]B. John Oommen, Richard K. S. Loke:
Probabilistic syntactic pattern recognition for traditional and generalized transposition errors. ICPR 1996: 685-689 - [c18]B. John Oommen, Rangasami L. Kashyap:
Optimal and Information Theoretic Syntactic Pattern Recognition for Traditional Errors. SSPR 1996: 11-20 - 1995
- [j48]B. John Oommen:
String Alignment with Substitution, Insertion, Deletion, Squashing and Expansion Operations. Inf. Sci. 83(1&2): 89-107 (1995) - [j47]S. T. Sum, B. John Oommen:
Mixture decomposition for distributions from the exponential family using a generalized method of moments. IEEE Trans. Syst. Man Cybern. 25(7): 1139-1149 (1995) - [j46]B. John Oommen, Hassan Masum:
Switching models for nonstationary random environments. IEEE Trans. Syst. Man Cybern. 25(9): 1334-1339 (1995) - [c17]B. John Oommen, I. Kuban Altinel, Necati Aras:
Arbitrary distance function estimation using vector quantization. ICNN 1995: 3062-3067 - [c16]B. John Oommen, R. K. S. Loke:
Noisy Subsequence Recognition Using Constrained String Editing Involving Substitutions, Insertions, Deletions and Generalized Transpositions. ICSC 1995: 116-123 - [c15]B. John Oommen, Edward V. de St. Croix:
On Using Learning Automata for Fast Graph Partitioning. LATIN 1995: 449-460 - 1994
- [j45]B. John Oommen, David T. H. Ng:
A New Technique for Enhancing Linked-List Data Retrieval: Reorganize Data Using Artificially synthesized Queries. Comput. J. 37(7): 598-609 (1994) - [j44]B. John Oommen, William Lee:
Constrained Tree Editing. Inf. Sci. 77(3-4): 253-273 (1994) - [j43]Jack R. Zgierski, B. John Oommen:
SEATER: An Object-Oriented Simulation Environment Using Learning Automata for Telephone Traffic Routing. IEEE Trans. Syst. Man Cybern. Syst. 24(2): 349-356 (1994) - 1993
- [j42]B. John Oommen:
Transforming Ill-Conditioned Constrained Problems using Projections. Comput. J. 36(3): 282-285 (1993) - [j41]B. John Oommen, Chris Fothergill:
Fast Learning Automaton-Based Image Examination and Retrieval. Comput. J. 36(6): 542-553 (1993) - [j40]Radhakrishna S. Valiveti, B. John Oommen:
Self-Organizing Doubly-Linked Lists. J. Algorithms 14(1): 88-114 (1993) - [j39]B. John Oommen, Jack R. Zgierski:
Breaking Substitution Cyphers Using Stochastic Automata. IEEE Trans. Pattern Anal. Mach. Intell. 15(2): 185-192 (1993) - [j38]Radhakrishna S. Valiveti, B. John Oommen:
Determining stochastic dependence for normally distributed vectors using the chi-squared metric. Pattern Recognit. 26(6): 975-987 (1993) - [j37]B. John Oommen, David T. H. Ng:
An Optimal Absorbing List Organization Strategy with Constant Memory Requirements. Theor. Comput. Sci. 119(2): 355-361 (1993) - [j36]Robert P. Cheetham, B. John Oommen, David T. H. Ng:
Adaptive Structuring of Binary Search Trees Using Conditional Rotations. IEEE Trans. Knowl. Data Eng. 5(4): 695-704 (1993) - [j35]David T. H. Ng, B. John Oommen, E. R. Hansen:
Adaptive learning mechanisms for ordering actions using random races. IEEE Trans. Syst. Man Cybern. 23(5): 1450-1465 (1993) - 1992
- [j34]David T. H. Ng, B. John Oommen:
A Short Note on Doubly-Linked List Reorganizing Heuristics. Comput. J. 35(5): 533-535 (1992) - [j33]B. John Oommen, Irwin Reichstein:
On the problem of multiple mobile robots cluttering a workspace. Inf. Sci. 63(1-2): 55-85 (1992) - [j32]Radhakrishna S. Valiveti, B. John Oommen:
On using the chi-squared metric for determining stochastic dependence. Pattern Recognit. 25(11): 1389-1400 (1992) - [j31]J. Kevin Lanctôt, B. John Oommen:
Discretized estimator learning automata. IEEE Trans. Syst. Man Cybern. 22(6): 1473-1483 (1992) - [p1]B. John Oommen, David T. H. Ng:
Enhancing Data Retrieval using Artificially synthesized Queries. Computer Science and Operations Research 1992: 513-532 - 1991
- [j30]B. John Oommen, Daniel C. Y. Ma:
Stochastic Automata Solutions to the Object Partitioning Problem. Comput. J. 34(Additional-Papers): A105-A120 (1991) - [j29]B. John Oommen, Nicte Andrade, S. Sitharama Iyengar:
Trajectory Planning of Robot Manipulators in Noisy Work Spaces Using Stochastic Automata. Int. J. Robotics Res. 10(2): 135-148 (1991) - [j28]Radhakrishna S. Valiveti, B. John Oommen:
Recognizing Sources of Random Strings. IEEE Trans. Pattern Anal. Mach. Intell. 13(4): 386-394 (1991) - [j27]B. John Oommen, Radhakrishna S. Valiveti, Jack R. Zgierski:
An adaptive learning solution to the keyboard optimization problem. IEEE Trans. Syst. Man Cybern. 21(6): 1608-1618 (1991) - [c14]Radhakrishna S. Valiveti, B. John Oommen, Jack R. Zgierski:
Adaptive Linear List Reorganization for a System Processing Set Queries. FCT 1991: 405-414 - 1990
- [j26]B. John Oommen, David T. H. Ng:
On Generating Random Permutations with Arbitrary Distributions. Comput. J. 33(4): 368-374 (1990) - [j25]B. John Oommen, E. R. Hansen, J. Ian Munro:
Deterministic Optimal and Expedient Move-to-Rear List Organizing Strategies. Theor. Comput. Sci. 74(2): 183-197 (1990) - [j24]B. John Oommen, J. Kevin Lanctôt:
Discretized pursuit learning automata. IEEE Trans. Syst. Man Cybern. 20(4): 931-938 (1990) - [j23]Jens Peter Reus Christensen, B. John Oommen:
Epsilon-optimal stubborn learning mechanisms. IEEE Trans. Syst. Man Cybern. 20(5): 1209-1216 (1990) - [c13]B. John Oommen, Radhakrishna S. Valiveti, Jack R. Zgierski:
A Fast Learning Automaton Solution to the Keyboard Optimization Problem. IEA/AIE (Vol. 2) 1990: 981-990
1980 – 1989
- 1989
- [c12]B. John Oommen, David T. H. Ng:
On Generating Random Permutations with Arbitrary Distributions. ACM Conference on Computer Science 1989: 27-32 - [c11]David T. H. Ng, B. John Oommen:
Generalizing Singly-Linked List Reorganizing Heuristics for Doubly-Linked Lists. MFCS 1989: 380-389 - [c10]B. John Oommen, David T. H. Ng:
Optimal Constant Space Move-to-Rear List Organization. Optimal Algorithms 1989: 115-125 - [c9]B. John Oommen, J. Kevin Lanctôt:
Epsilon-optimal discretized pursuit learning automata. SMC 1989: 6-12 - [c8]Jens Peter Reus Christensen, B. John Oommen:
On using distribution theory to prove the epsilon-optimality of stubborn learning mechanisms. SMC 1989: 286-291 - 1988
- [j22]B. John Oommen:
Correction to "Recognition of Noisy Subsequences Using Constrained Edit Distances". IEEE Trans. Pattern Anal. Mach. Intell. 10(6): 983-984 (1988) - [j21]B. John Oommen, Daniel C. Y. Ma:
Deterministic Learning Automata Solutions to the Equipartitioning Problem. IEEE Trans. Computers 37(1): 2-13 (1988) - [j20]Nageswara S. V. Rao, S. Sitharama Iyengar, B. John Oommen, Rangasami L. Kashyap:
On terrain acquisition by a point robot amidst polyhedral obstacles. IEEE J. Robotics Autom. 4(4): 450-455 (1988) - [j19]B. John Oommen, Jens Peter Reus Christensen:
ϵ-optimal discretized linear reward-penalty learning automata. IEEE Trans. Syst. Man Cybern. 18(3): 451-458 (1988) - [c7]Robert P. Cheetham, B. John Oommen, David T. H. Ng:
On Using Conditional Rotation Operations to Adaptively Structure Binary Search Trees. ICDT 1988: 161-175 - 1987
- [j18]B. John Oommen:
An Efficient Geometric Solution to the Minimum Spanning Circle Problem. Oper. Res. 35(1): 80-86 (1987) - [j17]B. John Oommen:
Recognition of Noisy Subsequences Using Constrained Edit Distances. IEEE Trans. Pattern Anal. Mach. Intell. 9(5): 676-685 (1987) - [j16]B. John Oommen, Irwin Reichstein:
On the problem of translating an elliptic object through a workspace of elliptic obstacles. Robotica 5(3): 187-196 (1987) - [j15]B. John Oommen, E. R. Hansen:
List Organizing Strategies Using Stochastic Move-to-Front and Stochastic Move-to-Rear Operations. SIAM J. Comput. 16(4): 705-716 (1987) - [j14]B. John Oommen, S. Sitharama Iyengar, Nageswara S. V. Rao, Rangasami L. Kashyap:
Robot navigation in unknown terrains using learned visibility graphs. Part I: The disjoint convex obstacle case. IEEE J. Robotics Autom. 3(6): 672-681 (1987) - [j13]B. John Oommen:
Ergodic Learning Automata Capable of Incorporating a Priori Information. IEEE Trans. Syst. Man Cybern. 17(4): 717-723 (1987) - [c6]B. John Oommen, Daniel C. Y. Ma:
Fast Object Partitioning Using Stochastic Learning Automata. SIGIR 1987: 111-122 - 1986
- [j12]B. John Oommen:
Constrained string editing. Inf. Sci. 40(3): 267-284 (1986) - [j11]B. John Oommen:
Absorbing and Ergodic Discretized Two-Action Learning Automata. IEEE Trans. Syst. Man Cybern. 16(2): 282-293 (1986) - [j10]B. John Oommen:
A Learning Automaton Solution to the Stochastic Minimum-Spanning Circle Problem. IEEE Trans. Syst. Man Cybern. 16(4): 598-603 (1986) - [c5]B. John Oommen, S. Sitharama Iyengar, Nageswara S. V. Rao, Rangasami L. Kashyap:
Robot Navigation in Unknown Terrains of Convex Polygonal Obstacles Using Learned Visibility Graphs. AAAI 1986: 1101-1106 - [c4]B. John Oommen, E. R. Hansen:
Expedient Stochastic Move-to-Front and optimal Move-to-Rear List Organizing Strategies. ICDT 1986: 349-364 - [c3]B. John Oommen, Irwin Reichstein:
On translating ellipses amidst elliptic obstacles. ICRA 1986: 1755-1760 - 1985
- [j9]B. John Oommen, M. A. L. Thathachar:
Multiaction learning automata possessing ergodicity of the mean. Inf. Sci. 35(3): 183-198 (1985) - [c2]B. John Oommen:
On the Futility of Arbitrarily Increasing Memory Capabilities of Stochastic Learning Automata. CAIA 1985: 308-312 - 1984
- [j8]Rangasami L. Kashyap, B. John Oommen:
Spelling correction using probabilistic methods. Pattern Recognit. Lett. 2(3): 147-154 (1984) - [j7]B. John Oommen, E. R. Hansen:
The asymptotic optimality of discretized linear reward-inaction learning automata. IEEE Trans. Syst. Man Cybern. 14(3): 542-545 (1984) - [c1]B. John Oommen:
Algorithms for String Editing which Permit Arbitrarily Complex Editing Constraints. MFCS 1984: 443-451 - 1983
- [j6]Rangasami L. Kashyap, B. John Oommen:
Scale Preserving Smoothing of Polygons. IEEE Trans. Pattern Anal. Mach. Intell. 5(6): 667-671 (1983) - [j5]Rangasami L. Kashyap, B. John Oommen:
The Noisy Substring Matching Problem. IEEE Trans. Software Eng. 9(3): 365-370 (1983) - [j4]M. A. L. Thathachar, B. John Oommen:
Learning automata processing ergodicity of the mean: The two-action case. IEEE Trans. Syst. Man Cybern. 13(6): 1143-1148 (1983) - 1982
- [j3]Rangasami L. Kashyap, B. John Oommen:
A Geometrical Approach to Polygonal Dissimilarity and Shape Matching. IEEE Trans. Pattern Anal. Mach. Intell. 4(6): 649-654 (1982) - 1981
- [j2]Rangasami L. Kashyap, B. John Oommen:
An effective algorithm for string correction using generalized edit distances--I. Description of the algorithm and its optimality. Inf. Sci. 23(2): 123-142 (1981) - [j1]Rangasami L. Kashyap, B. John Oommen:
An effective algorithm for string correction using generalized edit distance - II. Computational complexity of the algorithm and some applications. Inf. Sci. 23(3): 201-217 (1981)
Coauthor Index
[j177] [c184] [j171] [c180] [j160] [j158] [j157] [j156] [j151] [j144] [c152] [c151] [c150] [j143] [j142] [j136] [c142] [c141] [j134] [j132] [c136] [c134] [c133] [c132] [j127] [j124] [c129] [c128] [c127] [c125] [c124] [j122] [j118] [c119] [c117] [c116] [c114] [c110] [p2] [j104] [c103] [j97] [j95] [c97] [c94] [c91] [c85] [c81] [c80]
Luis Rueda 0001
aka: Luís G. Rueda
aka: Luís G. Rueda
[j191] [j189] [j185] [c199] [c196] [i2] [j179] [j176] [j175] [j174] [j172] [j168] [j167] [j165] [c178] [j162] [j161] [j159] [c177] [c176] [c175] [c173] [c171] [j156] [j153] [c169] [c167] [c165] [c164] [c162] [c159] [c154] [c153] [j144] [c152] [j136] [j134] [j132] [c136] [c134] [c133] [j124] [c129] [c128] [c127] [c117] [c116]
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