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2020 – today
- 2024
[j64]Kai Tanaka
, Mineichi Kudo, Keigo Kimura:
Sensor Data Simulation for Anomaly Detection of the Elderly Living Alone. IEEE Internet Things J. 11(19): 31675-31686 (2024)- [j63]Jiaqi Bao, Mineichi Kudo, Keigo Kimura, Lu Sun:
Redirected transfer learning for robust multi-layer subspace learning. Pattern Anal. Appl. 27(1): 25 (2024) - [j62]Jiaqi Bao, Mineichi Kudo, Keigo Kimura, Lu Sun:
Robust embedding regression for semi-supervised learning. Pattern Recognit. 145: 109894 (2024) - [c133]Kai Tanaka, Mineichi Kudo, Keigo Kimura:
Fall Detection by Ambient Sensors on Years-Long Simulation Data. ABC 2024: 1-8 - [c132]Genji Ohara, Keigo Kimura, Mineichi Kudo:
R-LIME: Rectangular Constraints and Optimization for Local Interpretable Model-agnostic Explanation Methods. ICPR (15) 2024: 80-95 - 2023
- [c131]Luhuan Fei, Lu Sun, Mineichi Kudo, Keigo Kimura:
Structured Sparse Multi-Task Learning with Generalized Group Lasso. ECAI 2023: 692-699 - [c130]Jiachun Jin, Jiankun Wang, Lu Sun, Jie Zheng, Mineichi Kudo:
Grouped Multi-Task Learning with Hidden Tasks Enhancement. ECAI 2023: 1164-1171 - [c129]Weijia Lin, Jiankun Wang, Lu Sun, Mineichi Kudo, Keigo Kimura:
Multi-Label Personalized Classification via Exclusive Sparse Tensor Factorization. ICDM 2023: 398-407 - [c128]Haruhi Mizuguchi, Keigo Kimura, Mineichi Kudo, Lu Sun:
Partial Multi-label Learning with a Few Accurately Labeled Data. PRICAI (2) 2023: 79-90 - [c127]Zhiwei Li, Zijian Yang, Lu Sun, Mineichi Kudo, Keigo Kimura:
Incomplete Multi-view Weak-Label Learning with Noisy Features and Imbalanced Labels. PRICAI (2) 2023: 124-130 - [i4]Kai Tanaka, Mineichi Kudo, Keigo Kimura:
Sensor Data Simulation for Anomaly Detection of the Elderly Living Alone. CoRR abs/2312.16852 (2023) - 2022
- [j61]Mariko Tai, Mineichi Kudo, Akira Tanaka, Hideyuki Imai, Keigo Kimura:
Kernelized Supervised Laplacian Eigenmap for Visualization and Classification of Multi-Label Data. Pattern Recognit. 123: 108399 (2022) - [c126]Kai Tanaka, Mineichi Kudo, Keigo Kimura:
Sensor Data Simulation with Wandering Behavior for the Elderly Living Alone. ICPR 2022: 885-891 - [c125]Shumpei Morishita, Mineichi Kudo, Keigo Kimura, Lu Sun:
Realization of Autoencoders by Kernel Methods. S+SSPR 2022: 1-10 - [c124]Keigo Kimura, Jiaqi Bao, Mineichi Kudo, Lu Sun:
Retargeted Regression Methods for Multi-label Learning. S+SSPR 2022: 203-212 - [c123]Mineichi Kudo, Keigo Kimura, Shumpei Morishita, Lu Sun:
Efficient Leave-One-Out Evaluation of Kernelized Implicit Mappings. S+SSPR 2022: 223-232 - [i3]Zhiwei Li, Lu Sun, Mineichi Kudo, Kego Kimura:
CEMENT: Incomplete Multi-View Weak-Label Learning with Long-Tailed Labels. CoRR abs/2201.01079 (2022) - 2021
- [j60]Kejing Lu, Mineichi Kudo:
AdaLSH: Adaptive LSH for Solving c-Approximate Maximum Inner Product Search Problem. IEICE Trans. Inf. Syst. 104-D(1): 138-145 (2021) - [j59]Kejing Lu, Mineichi Kudo, Chuan Xiao, Yoshiharu Ishikawa:
HVS: Hierarchical Graph Structure Based on Voronoi Diagrams for Solving Approximate Nearest Neighbor Search. Proc. VLDB Endow. 15(2): 246-258 (2021) - [c122]Kejing Lu, Mineichi Kudo:
MLSH: Mixed Hash Function Family for Approximate Nearest Neighbor Search in Multiple Fractional Metrics. DASFAA (2) 2021: 569-584 - [c121]Shuhei Aoki, Mineichi Kudo:
Balancing of Samples in Class Hierarchy. IWAIPR 2021: 219-228 - [c120]Tomoya Horio, Mineichi Kudo:
Feature Selection with Class Hierarchy for Imbalance Problems. IWAIPR 2021: 229-238 - [c119]Yasuyuki Kaneko, Mineichi Kudo:
SVM Based EVM for Open Space Problems. IWAIPR 2021: 239-248 - 2020
- [j58]Kejing Lu, Hongya Wang, Wei Wang, Mineichi Kudo:
VHP: Approximate Nearest Neighbor Search via Virtual Hypersphere Partitioning. Proc. VLDB Endow. 13(9): 1443-1455 (2020) - [c118]Mitsuki Maekawa, Atsuyoshi Nakamura, Mineichi Kudo:
Data-Dependent Conversion to a Compact Integer-Weighted Representation of a Weighted Voting Classifier. ACML 2020: 241-256 - [c117]Kejing Lu, Mineichi Kudo:
R2LSH: A Nearest Neighbor Search Scheme Based on Two-dimensional Projected Spaces. ICDE 2020: 1045-1056
2010 – 2019
- 2019
- [j57]Lu Sun, Mineichi Kudo:
Multi-label classification by polytree-augmented classifier chains with label-dependent features. Pattern Anal. Appl. 22(3): 1029-1049 (2019) - [c116]Mariko Tai, Mineichi Kudo:
A Supervised Laplacian Eigenmap Algorithm for Visualization of Multi-label Data: SLE-ML. CIARP 2019: 525-534 - 2018
- [j56]Ryo Watanabe, Junpei Komiyama, Atsuyoshi Nakamura, Mineichi Kudo:
UCB-SC: A Fast Variant of KL-UCB-SC for Budgeted Multi-Armed Bandit Problem. IEICE Trans. Fundam. Electron. Commun. Comput. Sci. 101-A(3): 662-667 (2018) - [j55]Lu Sun, Mineichi Kudo:
Optimization of classifier chains via conditional likelihood maximization. Pattern Recognit. 74: 503-517 (2018) - 2017
- [j54]Jana Backhus, Ichigaku Takigawa, Hideyuki Imai, Mineichi Kudo, Masanori Sugimoto:
An Online Self-Constructive Normalized Gaussian Network with Localized Forgetting. IEICE Trans. Fundam. Electron. Commun. Comput. Sci. 100-A(3): 865-876 (2017) - [j53]Koji Tabata, Atsuyoshi Nakamura, Mineichi Kudo:
An Efficient Approximate Algorithm for the 1-Median Problem on a Graph. IEICE Trans. Inf. Syst. 100-D(5): 994-1002 (2017) - [j52]Lu Sun, Mineichi Kudo, Keigo Kimura:
READER: Robust Semi-Supervised Multi-Label Dimension Reduction. IEICE Trans. Inf. Syst. 100-D(10): 2597-2604 (2017) - [j51]Ryo Watanabe, Junpei Komiyama, Atsuyoshi Nakamura, Mineichi Kudo:
KL-UCB-Based Policy for Budgeted Multi-Armed Bandits with Stochastic Action Costs. IEICE Trans. Fundam. Electron. Commun. Comput. Sci. 100-A(11): 2470-2486 (2017) - [i2]Hiroyuki Hanada, Mineichi Kudo, Atsuyoshi Nakamura:
On Practical Accuracy of Edit Distance Approximation Algorithms. CoRR abs/1701.06134 (2017) - [i1]Keigo Kimura, Lu Sun, Mineichi Kudo:
MLC Toolbox: A MATLAB/OCTAVE Library for Multi-Label Classification. CoRR abs/1704.02592 (2017) - 2016
- [j50]Atsuyoshi Nakamura, Ichigaku Takigawa, Hisashi Tosaka, Mineichi Kudo, Hiroshi Mamitsuka:
Mining approximate patterns with frequent locally optimal occurrences. Discret. Appl. Math. 200: 123-152 (2016) - [j49]Keigo Kimura, Mineichi Kudo, Yuzuru Tanaka:
A column-wise update algorithm for nonnegative matrix factorization in Bregman divergence with an orthogonal constraint. Mach. Learn. 103(2): 285-306 (2016) - [j48]Guoliang Lu, Yiqi Zhou, Xueyong Li, Mineichi Kudo:
Efficient action recognition via local position offset of 3D skeletal body joints. Multim. Tools Appl. 75(6): 3479-3494 (2016) - [j47]Sadamori Koujaku, Ichigaku Takigawa, Mineichi Kudo, Hideyuki Imai:
Dense core model for cohesive subgraph discovery. Soc. Networks 44: 143-152 (2016) - [j46]Hideaki Konno, Mineichi Kudo, Hideyuki Imai, Masanori Sugimoto:
Whisper to normal speech conversion using pitch estimated from spectrum. Speech Commun. 83: 10-20 (2016) - [c115]Lu Sun, Mineichi Kudo, Keigo Kimura:
A Scalable Clustering-Based Local Multi-Label Classification Method. ECAI 2016: 261-268 - [c114]Jana Backhus, Ichigaku Takigawa, Hideyuki Imai, Mineichi Kudo, Masanori Sugimoto:
Reducing Redundancy with Unit Merging for Self-constructive Normalized Gaussian Networks. ICANN (1) 2016: 444-452 - [c113]Jana Backhus, Ichigaku Takigawa, Hideyuki Imai, Mineichi Kudo, Masanori Sugimoto:
Online EM for the Normalized Gaussian Network with Weight-Time-Dependent Updates. ICONIP (4) 2016: 538-546 - [c112]Keigo Kimura, Mineichi Kudo, Lu Sun, Sadamori Koujaku:
Fast random k-labELsets for large-scale multi-label classification. ICPR 2016: 438-443 - [c111]Lu Sun, Mineichi Kudo, Keigo Kimura:
Multi-label classification with meta-label-specific features. ICPR 2016: 1612-1617 - [c110]Syota Suzuuchi, Mineichi Kudo:
Location-associated indoor behavior analysis of multiple persons. ICPR 2016: 2079-2084 - [c109]Batzaya Norov-Erdene, Mineichi Kudo, Lu Sun, Keigo Kimura:
Locality in multi-label classification problems. ICPR 2016: 2319-2324 - [c108]Mineichi Kudo, Keigo Kimura, Michal Haindl, Hiroshi Tenmoto:
Simultaneous visualization of samples, features and multi-labels. ICPR 2016: 3603-3608 - [c107]Shunsuke Suzuki, Mineichi Kudo, Atsuyoshi Nakamura:
Sitting posture diagnosis using a pressure sensor mat. ISBA 2016: 1-6 - [c106]Keigo Kimura, Mineichi Kudo, Lu Sun:
Simultaneous Nonlinear Label-Instance Embedding for Multi-label Classification. S+SSPR 2016: 15-25 - 2015
- [j45]Akira Tanaka, Hirofumi Takebayashi, Ichigaku Takigawa, Hideyuki Imai, Mineichi Kudo:
Ensemble and Multiple Kernel Regressors: Which Is Better? IEICE Trans. Fundam. Electron. Commun. Comput. Sci. 98-A(11): 2315-2324 (2015) - [j44]Ryo Watanabe, Atsuyoshi Nakamura, Mineichi Kudo:
An improved upper bound on the expected regret of UCB-type policies for a matching-selection bandit problem. Oper. Res. Lett. 43(6): 558-563 (2015) - [j43]Xavier Lladó, Atsushi Imiya, David Mason, Constantino Carlos Reyes-Aldasoro, Kazuaki Aoki, Mineichi Kudo, Yu-Jin Zhang, Vasileios Argyriou:
Corrigendum to 'Homage to Professor Maria Petrou' [ Pattern Recognition Letters 48 (2014) 2-7]. Pattern Recognit. Lett. 54: 109 (2015) - [j42]Shuai Tao, Mineichi Kudo, Bingnan Pei, Hidetoshi Nonaka, Jun Toyama:
Multiperson Locating and Their Soft Tracking in a Binary Infrared Sensor Network. IEEE Trans. Hum. Mach. Syst. 45(5): 550-561 (2015) - [c105]Michal Haindl, Stanislav Mikes, Mineichi Kudo:
Unsupervised Surface Reflectance Field Multi-segmenter. CAIP (1) 2015: 261-273 - [c104]Koji Tabata, Atsuyoshi Nakamura, Mineichi Kudo:
An Algorithm for Influence Maximization in a Two-Terminal Series Parallel Graph and its Application to a Real Network. Discovery Science 2015: 275-283 - [c103]Keigo Kimura, Mineichi Kudo:
Variable Selection for Efficient Nonnegative Tensor Factorization. ICDM 2015: 805-810 - [c102]Lu Sun, Mineichi Kudo:
Polytree-Augmented Classifier Chains for Multi-Label Classification. IJCAI 2015: 3834-3840 - [c101]Ayako Mikami, Mineichi Kudo, Atsuyoshi Nakamura:
Diversity Measures and Margin Criteria in Multi-class Majority Vote Ensemble. MCS 2015: 27-37 - [c100]Sadamori Koujaku, Mineichi Kudo, Ichigaku Takigawa, Hideyuki Imai:
Community Change Detection in Dynamic Networks in Noisy Environment. WWW (Companion Volume) 2015: 793-798 - 2014
- [j41]Guoliang Lu, Mineichi Kudo:
Learning action patterns in difference images for efficient action recognition. Neurocomputing 123: 328-336 (2014) - [j40]Tomomi Endo, Kazuhiro Omura, Mineichi Kudo:
Analysis of Relationship between RéNyi Entropy and Marginal Bayes error and its Application to Weighted naïVE Bayes Classifiers. Int. J. Pattern Recognit. Artif. Intell. 28(7) (2014) - [j39]Koji Ouchi, Atsuyoshi Nakamura, Mineichi Kudo:
An efficient construction and application usefulness of rectangle greedy covers. Pattern Recognit. 47(3): 1459-1468 (2014) - [j38]Hiroyuki Hanada, Mineichi Kudo, Atsuyoshi Nakamura:
Average-case linear-time similar substring searching by the q-gram distance. Theor. Comput. Sci. 530: 23-41 (2014) - [c99]Anton Milan, Stefan Roth, Konrad Schindler, Mineichi Kudo:
Privacy Preserving Multi-target Tracking. ACCV Workshops (3) 2014: 519-530 - [c98]Keigo Kimura, Yuzuru Tanaka, Mineichi Kudo:
A Fast Hierarchical Alternating Least Squares Algorithm for Orthogonal Nonnegative Matrix Factorization. ACML 2014 - [c97]Akira Tanaka, Ichigaku Takigawa, Hideyuki Imai, Mineichi Kudo:
Theoretical Analyses on Ensemble and Multiple Kernel Regressors. ACML 2014 - [c96]Kenshiro Nishikawa, Mineichi Kudo:
Group Sleepiness Measurement in Classroom. AMMDS 2014: 64-72 - [c95]Hideaki Konno, Rinako Sato, Hideyuki Imai, Mineichi Kudo:
Deterioration of intelligibility in whispered Japanese speech. APSIPA 2014: 1-4 - [c94]Hiroshi Tsukioka, Mineichi Kudo:
Selection of Features in Accord with Population Drift. ICPR 2014: 1591-1596 - [c93]Akira Tanaka, Ichigaku Takigawa, Hideyuki Imai, Mineichi Kudo:
Analyses on Generalization Error of Ensemble Kernel Regressors. S+SSPR 2014: 273-281 - 2013
- [j37]Atsuyoshi Nakamura, Tomoya Saito, Ichigaku Takigawa, Mineichi Kudo, Hiroshi Mamitsuka:
Fast algorithms for finding a minimum repetition representation of strings and trees. Discret. Appl. Math. 161(10-11): 1556-1575 (2013) - [j36]Guoliang Lu, Mineichi Kudo:
Self-Similarities in Difference Images: A New Cue for Single-Person Oriented Action Recognition. IEICE Trans. Inf. Syst. 96-D(5): 1238-1242 (2013) - [j35]Guoliang Lu, Mineichi Kudo, Jun Toyama:
Temporal segmentation and assignment of successive actions in a long-term video. Pattern Recognit. Lett. 34(15): 1936-1944 (2013) - [c92]Yingmei Piao, Mineichi Kudo:
How Do Facial Expressions Contribute to Age Prediction? ACPR 2013: 882-886 - [c91]Hideaki Konno, Hideo Kanemitsu, Nobuyuki Takahashi, Mineichi Kudo:
Acoustic characteristics related to the perceptual pitch in whispered vowels. ASRU 2013: 245-249 - [c90]Tomomi Endo, Mineichi Kudo:
Weighted Naïve Bayes Classifiers by Renyi Entropy. CIARP (1) 2013: 149-156 - 2012
- [j34]Guoliang Lu, Mineichi Kudo, Jun Toyama:
Selection of Characteristic Frames in Video for Efficient Action Recognition. IEICE Trans. Inf. Syst. 95-D(10): 2514-2521 (2012) - [j33]Shen Pan, Mineichi Kudo:
Recognition of Wood Porosity Based on Direction Insensitive Feature Sets. Trans. Mach. Learn. Data Min. 5(1): 45-62 (2012) - [j32]Shuai Tao, Mineichi Kudo, Hidetoshi Nonaka:
Privacy-Preserved Behavior Analysis and Fall Detection by an Infrared Ceiling Sensor Network. Sensors 12(12): 16920-16936 (2012) - [c89]Koji Tabata, Atsuyoshi Nakamura, Mineichi Kudo:
Fast Approximation Algorithm for the 1-Median Problem. Discovery Science 2012: 169-183 - [c88]Shuai Tao, Mineichi Kudo, Hidetoshi Nonaka, Jun Toyama:
Camera view usage of binary infrared sensors for activity recognition. ICPR 2012: 1759-1762 - [c87]Guoliang Lu, Mineichi Kudo, Jun Toyama:
Action recognition via sparse representation of characteristic frames. ICPR 2012: 3268-3271 - [c86]Atsuyoshi Nakamura, Hisashi Tosaka, Mineichi Kudo:
Frequent Approximate Substring Pattern Mining Using Locally Optimal Occurrence Counting. IIAI-AAI 2012: 54-59 - [c85]Hironobu Yasuda, Mineichi Kudo:
Speech rate change detection in martingale framework. ISDA 2012: 859-864 - [c84]Kazuhiro Omura, Mineichi Kudo, Tomomi Endo, Tetsuya Murai:
Weighted naïve Bayes classifier on categorical features. ISDA 2012: 865-870 - [c83]Akira Tanaka, Ichigaku Takigawa, Hideyuki Imai, Mineichi Kudo:
Extended Analyses for an Optimal Kernel in a Class of Kernels with an Invariant Metric. SSPR/SPR 2012: 345-353 - [e2]Georgy L. Gimel'farb, Edwin R. Hancock, Atsushi Imiya, Arjan Kuijper, Mineichi Kudo, Shinichiro Omachi, Terry Windeatt, Keiji Yamada:
Structural, Syntactic, and Statistical Pattern Recognition - Joint IAPR International Workshop, SSPR&SPR 2012, Hiroshima, Japan, November 7-9, 2012. Proceedings. Lecture Notes in Computer Science 7626, Springer 2012, ISBN 978-3-642-34165-6 [contents] - 2011
- [j31]Tetsuji Takahashi, Mineichi Kudo, Atsuyoshi Nakamura:
Construction of convex hull classifiers in high dimensions. Pattern Recognit. Lett. 32(16): 2224-2230 (2011) - [c82]Shuai Tao, Mineichi Kudo, Hidetoshi Nonaka, Jun Toyama:
Person Authentication and Activities Analysis in an Office Environment Using a Sensor Network. AmI Workshops 2011: 119-127 - [c81]Shuai Tao, Mineichi Kudo, Hidetoshi Nonaka, Jun Toyama:
Person Localization and Soft Authentication Using an Infrared Ceiling Sensor Network. CAIP (2) 2011: 122-129 - [c80]Guoliang Lu, Mineichi Kudo, Jun Toyama:
Hierarchical Foreground Detection in Dynamic Background. CAIP (2) 2011: 413-420 - [c79]Hiroyuki Hanada, Atsuyoshi Nakamura, Mineichi Kudo:
A practical comparison of edit distance approximation algorithms. GrC 2011: 231-236 - [c78]Guoliang Lu, Mineichi Kudo, Jun Toyama:
Robust human pose estimation from corrupted images with partial occlusions and noise pollutions. GrC 2011: 433-438 - [c77]Hisataka Nakane, Jun Toyama, Mineichi Kudo:
Fatigue detection using a pressure sensor chair. GrC 2011: 490-495 - [c76]Kazuhiro Omura, Kazuaki Aoki, Mineichi Kudo:
Attribute value reduction for gaining simpler rules. GrC 2011: 527-532 - [c75]Koji Ouchi, Atsuyoshi Nakamura, Mineichi Kudo:
Efficient construction and usefulness of hyper-rectangle greedy covers. GrC 2011: 533-538 - [c74]Shuai Tao, Mineichi Kudo, Hidetoshi Nonaka, Jun Toyama:
Recording the Activities of Daily Living based on person localization using an infrared ceiling sensor network. GrC 2011: 647-652 - [c73]Akira Tanaka, Hideyuki Imai, Mineichi Kudo, Masaaki Miyakoshi:
Theoretical analyses on a class of nested RKHS's. ICASSP 2011: 2072-2075 - [c72]Shen Pan, Mineichi Kudo:
Recognition of Porosity in Wood Microscopic Anatomical Images. ICDM 2011: 147-160 - [c71]Atsuyoshi Nakamura, Mineichi Kudo:
Packing Alignment: Alignment for Sequences of Various Length Events. PAKDD (2) 2011: 234-245 - [c70]Hidetoshi Nonaka, Shuai Tao, Jun Toyama, Mineichi Kudo:
Ceiling Sensor Network for Soft Authentication and Person Tracking using Equilibrium Line. PECCS 2011: 218-223 - [e1]Tzung-Pei Hong, Yasuo Kudo, Mineichi Kudo, Tsau Young Lin, Been-Chian Chien, Shyue-Liang Wang, Masahiro Inuiguchi, Guilong Liu:
2011 IEEE International Conference on Granular Computing, GrC-2011, Kaohsiung, Taiwan, November 8-10, 2011. IEEE Computer Society 2011, ISBN 978-1-4577-0372-0 [contents] - 2010
- [j30]Jun Toyama, Mineichi Kudo, Hideyuki Imai:
Probably correct k-nearest neighbor search in high dimensions. Pattern Recognit. 43(4): 1361-1372 (2010) - [j29]Kenji Tabata, Maiko Sato, Mineichi Kudo:
Data compression by volume prototypes for streaming data. Pattern Recognit. 43(9): 3162-3176 (2010) - [c69]Taishi Uchiya, Atsuyoshi Nakamura, Mineichi Kudo:
Algorithms for Adversarial Bandit Problems with Multiple Plays. ALT 2010: 375-389 - [c68]Akira Tanaka, Hideyuki Imai, Mineichi Kudo, Masaaki Miyakoshi:
A Relationship Between Generalization Error and Training Samples in Kernel Regressors. ICPR 2010: 1421-1424 - [c67]Tetsuji Takahashi, Mineichi Kudo:
Margin Preserved Approximate Convex Hulls for Classification. ICPR 2010: 4052-4055 - [c66]Kazuaki Aoki, Mineichi Kudo:
A top-down construction of class decision trees with selected features and classifiers. HPCS 2010: 390-398 - [c65]Atsuyoshi Nakamura, Tomoya Saito, Ichigaku Takigawa, Hiroshi Mamitsuka, Mineichi Kudo:
Algorithms for Finding a Minimum Repetition Representation of a String. SPIRE 2010: 185-190 - [c64]Kazuki Tsuji, Mineichi Kudo, Akira Tanaka:
Localized Projection Learning. SSPR/SPR 2010: 90-99
2000 – 2009
- 2009
- [j28]Ichigaku Takigawa, Mineichi Kudo, Atsuyoshi Nakamura:
Convex sets as prototypes for classifying patterns. Eng. Appl. Artif. Intell. 22(1): 101-108 (2009) - [j27]Taisuke Hosokawa, Mineichi Kudo, Hidetoshi Nonaka, Jun Toyama:
Soft authentication using an infrared ceiling sensor network. Pattern Anal. Appl. 12(3): 237-249 (2009) - [j26]Masafumi Yamada, Kazuhiro Kamiya, Mineichi Kudo, Hidetoshi Nonaka, Jun Toyama:
Soft authentication and behavior analysis using a chair with sensors attached: hipprint authentication. Pattern Anal. Appl. 12(3): 251-260 (2009) - [c63]Tetsuji Takahashi, Mineichi Kudo, Atsuyoshi Nakamura:
Classifier Selection in a Family of Polyhedron Classifiers. CIARP 2009: 441-448 - [c62]Mineichi Kudo, Jun Toyama, Hideyuki Imai:
A Fast Nearest Neighbor Method Using Empirical Marginal Distribution. KES (2) 2009: 333-339 - [c61]Satoshi Shirai, Mineichi Kudo, Atsuyoshi Nakamura:
Comparison of Bagging and Boosting Algorithms on Sample and Feature Weighting. MCS 2009: 22-31 - [c60]Maiko Sato, Mineichi Kudo, Jun Toyama:
Clustering and Density Estimation for Streaming Data using Volume Prototypes. PRIS 2009: 39-48 - 2008
- [j25]Yohji Shidara, Mineichi Kudo, Atsuyoshi Nakamura:
Classification Based on Consistent Itemset Rules. Trans. Mach. Learn. Data Min. 1(1): 17-30 (2008) - [j24]Mineichi Kudo, Tetsuya Murai:
Extended DNF Expression and Variable Granularity in Information Tables. IEEE Trans. Fuzzy Syst. 16(2): 285-298 (2008) - [c59]Atsuyoshi Nakamura, Mineichi Kudo:
What Sperner Family Concept Class is Easy to Be Enumerated? ICDM 2008: 482-491 - [c58]Kazuhiro Kamiya, Mineichi Kudo, Hidetoshi Nonaka, Jun Toyama:
Sitting posture analysis by pressure sensors. ICPR 2008: 1-4 - [c57]Mineichi Kudo, Atsuyoshi Nakamura, Ichigaku Takigawa:
Classification by reflective convex hulls. ICPR 2008: 1-4 - [c56]Yohji Shidara, Mineichi Kudo, Atsuyoshi Nakamura:
Classification by bagged consistent itemset rules. ICPR 2008: 1-4 - [c55]Akira Tanaka, Hideyuki Imai, Mineichi Kudo, Masaaki Miyakoshi:
Optimal Kernel in a Class of Kernels with an Invariant Metric. SSPR/SPR 2008: 530-539 - [c54]Kazuaki Aoki, Mineichi Kudo:
Feature and Classifier Selection in Class Decision Trees. SSPR/SPR 2008: 562-571 - [c53]Hiroshi Tenmoto, Mineichi Kudo:
Soft Feature Selection by Using a Histogram-Based Classifier. SSPR/SPR 2008: 572-581 - [c52]Satoshi Shirai, Mineichi Kudo, Atsuyoshi Nakamura:
Bagging, Random Subspace Method and Biding. SSPR/SPR 2008: 801-810 - [c51]Maiko Sato, Mineichi Kudo, Jun Toyama:
Behavior Analysis of Volume Prototypes in High Dimensionality. SSPR/SPR 2008: 874-884 - 2007
- [j23]Akira Tanaka, Hideyuki Imai, Mineichi Kudo, Masaaki Miyakoshi:
Integrated kernels and their properties. Pattern Recognit. 40(11): 2930-2938 (2007) - [c50]Hisashi Tosaka, Atsuyoshi Nakamura, Mineichi Kudo:
Mining Subtrees with Frequent Occurrence of Similar Subtrees. Discovery Science 2007: 286-290 - [c49]Mineichi Kudo, Satoshi Shirai, Hiroshi Tenmoto:
A Combination of Sample Subsets and Feature Subsets in One-Against-Other Classifiers. MCS 2007: 241-250 - [c48]Yohji Shidara, Atsuyoshi Nakamura, Mineichi Kudo:
CCIC: Consistent Common Itemsets Classifier. MLDM 2007: 490-498 - [c47]Yuji Muto, Mineichi Kudo, Yohji Shidara:
Reduction of Categorical and Numerical Attribute Values for Understandability of Data and Rules. RSKT 2007: 211-218 - 2006
- [j22]Yuji Muto, Mineichi Kudo, Tetsuya Murai:
Reduction of Attribute Values for Kansei Representation. J. Adv. Comput. Intell. Intell. Informatics 10(5): 666-672 (2006) - [j21]Naoto Abe, Mineichi Kudo, Jun Toyama, Masaru Shimbo:
Classifier-independent feature selection on the basis of divergence criterion. Pattern Anal. Appl. 9(2-3): 127-137 (2006) - [j20]Naoto Abe, Mineichi Kudo:
Non-parametric classifier-independent feature selection. Pattern Recognit. 39(5): 737-746 (2006) - [c46]Masafumi Yamada, Mineichi Kudo, Hidetoshi Nonaka, Jun Toyama:
Hipprint Person Identification and Behavior Analys. ICPR (4) 2006: 533-536 - [c45]Akira Tanaka, Masashi Sugiyama, Hideyuki Imai, Mineichi Kudo, Masaaki Miyakoshi:
Model Selection Using a Class of Kernels with an Invariant Metric. SSPR/SPR 2006: 862-870 - 2005
- [j19]Kazuaki Aoki, Toshiharu Watanabe, Mineichi Kudo:
Design of decision trees using class-dependent feature subsets. Syst. Comput. Jpn. 36(4): 37-47 (2005) - [c44]Mineichi Kudo, Atsuyoshi Nakamura:
Specific-Purpose Web Searches on the Basis of Structure and Contents. Federation over the Web 2005: 79-96 - [c43]Hidehiko Ino, Mineichi Kudo, Atsuyoshi Nakamura:
A Comparative Study of Algorithms for Finding Web Communities. ICDE Workshops 2005: 1257 - [c42]Hiroyuki Hasegawa, Mineichi Kudo, Atsuyoshi Nakamura:
Empirical Study on Usefulness of Algorithm SACwRApper for Reputation Extraction from the WWW. KES (4) 2005: 668-674 - [c41]Taisuke Hosokawa, Mineichi Kudo:
Person Tracking with Infrared Sensors. KES (4) 2005: 682-688 - [c40]Naoto Abe, Mineichi Kudo:
Entropy Criterion for Classifier-Independent Feature Selection. KES (4) 2005: 689-695 - [c39]Hiroshi Tenmoto, Mineichi Kudo:
Finding and Auto-labeling of Task Groups on E-Mails and Documents. KES (4) 2005: 696-702 - [c38]Masafumi Yamada, Jun Toyama, Mineichi Kudo:
Person Recognition by Pressure Sensors. KES (4) 2005: 703-708 - [c37]Ichigaku Takigawa, Mineichi Kudo, Atsuyoshi Nakamura:
The Convex Subclass Method: Combinatorial Classifier Based on a Family of Convex Sets. MLDM 2005: 90-99 - [c36]Atsuyoshi Nakamura, Mineichi Kudo:
Mining Frequent Trees with Node-Inclusion Constraints. PAKDD 2005: 850-860 - [c35]Mineichi Kudo, Tetsuya Murai:
A New Treatment and Viewpoint of Information Tables. RSFDGrC (1) 2005: 234-243 - [c34]Yuji Muto, Mineichi Kudo:
Discernibility-Based Variable Granularity and Kansei Representations. RSFDGrC (1) 2005: 692-700 - [c33]Hiroshi Tenmoto, Mineichi Kudo:
Density- and Complexity-Regularization in Gaussian Mixture Bayesian Classifier. WSTST 2005: 391-399 - [c32]Hidehiko Ino, Mineichi Kudo, Atsuyoshi Nakamura:
Partitioning of Web graphs by community topology. WWW 2005: 661-669 - [p1]Yohji Shidara, Mineichi Kudo, Atsuyoshi Nakamura:
Extraction of Generalized Rules with Automated Attribute Abstraction. Foundations of Data Mining and knowledge Discovery 2005: 161-170 - 2004
- [j18]Ichigaku Takigawa, Mineichi Kudo, Jun Toyama:
Performance analysis of minimum ℓ1-norm solutions for underdetermined source separation. IEEE Trans. Signal Process. 52(3): 582-591 (2004) - [c31]Tetsuya Murai, Yasuo Kudo, Van-Nam Huynh, Akira Tanaka, Mineichi Kudo:
A note on fuzzy granular reasoning. FUZZ-IEEE 2004: 263-268 - [c30]Ichigaku Takigawa, Mineichi Kudo, Atsuyoshi Nakamura, Jun Toyama:
On the Minimum l1-Norm Signal Recovery in Underdetermined Source Separation. ICA 2004: 193-200 - [c29]Michal Haindl, Jirí Grim, Petr Somol, Pavel Pudil, Mineichi Kudo:
A Gaussian Mixture-Based Colour Texture Model. ICPR (3) 2004: 177-180 - [c28]Akira Tanaka, Ichigaku Takigawa, Hideyuki Imai, Mineichi Kudo, Masaaki Miyakoshi:
Projection Learning Based Kernel Machine Design Using Series of Monotone Increasing Reproducing Kernel Hilbert Spaces. KES 2004: 1058-1064 - [c27]Masafumi Yamada, Mineichi Kudo:
Combination of Weak Evidences by D-S Theory for Person Recognition. KES 2004: 1065-1071 - [c26]Tetsuya Murai, Masayuki Sanada, Yasuo Kudo, Mineichi Kudo:
A Note on Ziarko's Variable Precision Rough Set Model and Nonmonotonic Reasoning. Rough Sets and Current Trends in Computing 2004: 103-108 - [c25]Mineichi Kudo, Hideyuki Imai, Akira Tanaka, Tetsuya Murai:
A Nearest Neighbor Method Using Bisectors. SSPR/SPR 2004: 885-893 - [c24]Hiroshi Tenmoto, Yasukuni Mori, Mineichi Kudo:
Classifier-Independent Visualization of Supervised Data Structures Using a Graph. SSPR/SPR 2004: 1043-1051 - 2003
- [j17]Mineichi Kudo, Naoto Masuyama, Jun Toyama, Masaru Shimbo:
Simple termination conditions for k-nearest neighbor method. Pattern Recognit. Lett. 24(9-10): 1203-1213 (2003) - [c23]Atsuyoshi Nakamura, Mineichi Kudo, Akira Tanaka, Kazuhiko Tanabe:
Collaborative Filtering Using Projective Restoration Operators. Discovery Science 2003: 393-401 - [c22]Atsuyoshi Nakamura, Mineichi Kudo, Akira Tanaka:
Collaborative Filtering Using Restoration Operators. PKDD 2003: 339-349 - 2002
- [c21]Naoto Abe, Mineichi Kudo, Masaru Shimbo:
Classifier-Independent Feature Selection Based on Non-parametric Discriminant Analysis. SSPR/SPR 2002: 470-479 - [c20]Kazuaki Aoki, Mineichi Kudo:
Decision Tree Using Class-Dependent Feature Subsets. SSPR/SPR 2002: 761-769 - 2001
- [j16]Hiroki Hayashi, Mineichi Kudo, Jun Toyama, Masaru Shimbo:
Fast Labelling of Natural Scenes Using Enhanced Knowledge. Pattern Anal. Appl. 4(1): 20-27 (2001) - [j15]Yoshinori Yanagihara, Masanori Kawakami, Mineichi Kudo, Jun Toyama, Masaru Shimbo:
A two-channel coding of images using spline surfaces. Syst. Comput. Jpn. 32(6): 13-20 (2001) - 2000
- [j14]Mineichi Kudo, Jack Sklansky:
Comparison of algorithms that select features for pattern classifiers. Pattern Recognit. 33(1): 25-41 (2000) - [c19]Mineichi Kudo, Hideyuki Imai, Masaru Shimbo:
A Histogram-Based Classifier on Overlapped Bins. ICPR 2000: 2029-2033 - [c18]Hiroshi Tenmoto, Mineichi Kudo, Masaru Shimbo:
Selection of the Number of Components Using a Genetic Algorithm for Mixture Model Classifiers. SSPR/SPR 2000: 511-520 - [c17]Naoto Abe, Mineichi Kudo, Jun Toyama, Masaru Shimbo:
A Divergence Criterion for Classifier-Independent Feature Selection. SSPR/SPR 2000: 668-676 - [c16]Mineichi Kudo, Petr Somol, Pavel Pudil, Masaru Shimbo, Jack Sklansky:
Comparison of Classifier-Specific Feature Selection Algorithms. SSPR/SPR 2000: 677-686
1990 – 1999
- 1999
- [j13]Mineichi Kudo, Jun Toyama, Masaru Shimbo:
Multidimensional curve classification using passing-through regions. Pattern Recognit. Lett. 20(11-13): 1103-1111 (1999) - [c15]Hiroshi Tenmoto, Mineichi Kudo, Masaru Shimbo:
Determination of the number of components based on class separability in mixture-based classifiers. KES 1999: 439-442 - [c14]Naoto Masuyama, Mineichi Kudo, Jun Toyama, Masaru Shimbo:
Termination conditions for a fast k-nearest neighbor method. KES 1999: 443-446 - [c13]Hiroki Hayashi, Mineichi Kudo, Jun Toyama, Masaru Shimbo:
Estimation of velocity vectors from a video stream using discontinuity of optical flow. KES 1999: 447-450 - [c12]Masanori Kawakami, Mineichi Kudo, Jun Toyama, Masaru Shimbo:
Effective sampling points for two-channel spline image coding. KES 1999: 451-454 - [c11]Tomohiko Gotoh, Mineichi Kudo, Jun Toyama, Masaru Shimbo:
Geometry reconstruction of urban scenes by tracking vertical edges. KES 1999: 455-458 - [c10]J. Konishi, S. Shimba, Jun Toyama, Mineichi Kudo, Masaru Shimbo:
Tabu search for solving optimization problems on Hopfield neural networks. KES 1999: 518-521 - 1998
- [j12]Mineichi Kudo, Jack Sklansky:
A comparative evaluation of medium- and large-scale feature selectors for pattern classifiers. Kybernetika 34(4): 429-434 (1998) - [j11]Manabu Sato, Mineichi Kudo, Jun Toyama, Masaru Shimbo:
Construction of nonlinear discrimination function based on the MDL criterion. Kybernetika 34(4): 467-472 (1998) - [j10]Hiroshi Tenmoto, Mineichi Kudo, Masaru Shimbo:
Piecewise linear classifiers preserving high local recognition rates. Kybernetika 34(4): 479-484 (1998) - [j9]Hiroshi Tenmoto, Mineichi Kudo, Masaru Shimbo:
Piecewise linear classifiers with an appropriate number of hyperplanes. Pattern Recognit. 31(11): 1627-1634 (1998) - [j8]Mineichi Kudo, Yoichiro Torii, Yasukuni Mori, Masaru Shimbo:
Approximation of class regions by quasi convex hulls. Pattern Recognit. Lett. 19(9): 777-786 (1998) - [c9]Mineichi Kudo, Hiroshi Tenmoto, Satoru Sumiyoshi, Masaru Shimbo:
A subclass-based mixture model for pattern recognition. ICPR 1998: 870-872 - [c8]Yasukuni Mori, Mineichi Kudo, Jun Toyama, Masaru Shimbo:
Visualization of the structure of classes using a graph. ICPR 1998: 1724-1727 - [c7]Mineichi Kudo, F. Taniguchi, Hiroshi Tenmoto, Masaru Shimbo:
Appropriate initial component densities of mixture modeling for pattern recognition. KES (2) 1998: 216-220 - [c6]Shinichi Yanagi, Mineichi Kudo, Masaru Shimbo:
Polynomial-sample learnability about distance-0 and 1 DNF formulas. KES (2) 1998: 230-235 - [c5]Mineichi Kudo, Jack Sklansky:
Classifier-Independent Feature Selection For Two-Stage Feature Selection. SSPR/SPR 1998: 548-554 - [c4]Maiko Sato, Mineichi Kudo, Jun Toyama, Masaru Shimbo:
Feature Selection For a Nonlinear Classifier. SSPR/SPR 1998: 555-563 - [c3]Hiroshi Tenmoto, Mineichi Kudo, Masaru Shimbo:
MDL-Based Selection of the Number of Components in Mixture Models for Pattern Classification. SSPR/SPR 1998: 831-836 - 1997
- [c2]F. Taniguchi, Mineichi Kudo, Masaru Shimbo:
Estimation of class regions in feature space using rough set theory. KES (2) 1997: 373-377 - 1996
- [j7]Mineichi Kudo, Shinichi Yanagi, Masaru Shimbo:
Construction of class regions by a randomized algorithm: a randomized subclass method. Pattern Recognit. 29(4): 581-588 (1996) - [j6]Mineichi Kudo, Koji Mizukami, Yuji Nakamura, Masaru Shimbo:
Realization of membership quiries in character recognition. Pattern Recognit. Lett. 17(1): 77-82 (1996) - [c1]Mineichi Kudo, Masaru Shimbo:
Selection of classifiers based on the MDL principle using the VC dimension. ICPR 1996: 886-890 - 1993
- [j5]Mineichi Kudo, Masaru Shimbo:
Feature selection based on the structural indices of categories. Pattern Recognit. 26(6): 891-901 (1993) - 1992
- [j4]Mineichi Kudo, S. Kitamura-Abe, Masaru Shimbo, Y. Lida:
Analysis of context of 5'-splice site sequences in mammalian mRNA precursors by subclass method. Comput. Appl. Biosci. 8(4): 367-376 (1992)
1980 – 1989
- 1989
- [j3]Mineichi Kudo, Masaru Shimbo:
Optimal subclasses with dichotomous variables for feature selection and discrimination. IEEE Trans. Syst. Man Cybern. 19(5): 1194-1198 (1989) - 1988
- [j2]Mineichi Kudo, Masaru Shimbo:
Efficient regular grammatical inference techniques by the use of partial similarities and their logical relationships. Pattern Recognit. 21(4): 401-409 (1988) - 1987
- [j1]Mineichi Kudo, Y. Iida, Masaru Shimbo:
Syntactic pattern analysis of 5'-splice site sequences of mRNA precursors in higher eukaryote genes. Comput. Appl. Biosci. 3(4): 319-324 (1987)
Coauthor Index
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