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ORCIDRyohei Nakano
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2010 – 2019
- 2019
[c79]Seiya Satoh, Ryohei Nakano:
Faster RBF Network Learning Utilizing Singular Regions. ICPRAM 2019: 501-508- [c78]Ryohei Nakano, Seiya Satoh:
Mixture of Multilayer Perceptron Regressions. ICPRAM 2019: 509-516 - 2018
- [c77]Seiya Satoh, Ryohei Nakano:
A New Method for Learning RBF Networks by Utilizing Singular Regions. ICAISC (1) 2018: 214-225 - 2017
- [c76]Seiya Satoh, Ryohei Nakano:
How New Information Criteria WAIC and WBIC Worked for MLP Model Selection. ICPRAM 2017: 105-111 - [c75]Seiya Satoh, Ryohei Nakano:
Performance of Complex-Valued Multilayer Perceptrons Largely Depends on Learning Methods. IJCCI 2017: 45-53 - 2016
- [c74]Seiya Satoh, Ryohei Nakano:
How complex-valued multilayer perceptron can predict the behavior of deterministic chaos. IJCNN 2016: 4118-4124 - 2015
- [c73]Seiya Satoh, Ryohei Nakano:
A Yet Faster Version of Complex-valued Multilayer Perceptron Learning using Singular Regions and Search Pruning. IJCCI (NCTA) 2015: 122-129 - [c72]Seiya Satoh, Ryohei Nakano:
Complex-valued multilayer perceptron learning using singular regions and search pruning. IJCNN 2015: 1-6 - 2014
- [c71]Ryohei Nakano:
Emergent Induction of L-system Grammar from a String with Deletion-type Transmutation. KDIR 2014: 397-402 - [c70]Seiya Satoh, Ryohei Nakano:
Complex-Valued Multilayer Perceptron Search Utilizing Singular Regions of Complex-Valued Parameter Space. ICANN 2014: 315-322 - [c69]Seiya Satoh, Ryohei Nakano:
Singularity Stairs Following with Limited Numbers of Hidden Units. IJCCI (NCTA) 2014: 180-186 - 2013
- [j19]Seiya Satoh, Ryohei Nakano:
Fast and Stable Learning Utilizing Singular Regions of Multilayer Perceptron. Neural Process. Lett. 38(2): 99-115 (2013) - [c68]Ryohei Nakano:
Emergent Induction of Deterministic Context-Free L-system Grammar. IBICA 2013: 75-84 - 2012
- [c67]Shinya Suzumura, Ryohei Nakano:
Complex-Valued Multilayer Perceptron Search Utilizing Eigen Vector Descent and Reducibility Mapping. ICANN (2) 2012: 1-8 - 2011
- [c66]Ryohei Nakano, Seiya Satoh, Takayuki Ohwaki:
Learning Method Utilizing Singular Region of Multilayer Perceptron. IJCCI (NCTA) 2011: 106-111 - 2010
- [j18]Masahiro Kimura, Kazumi Saito, Ryohei Nakano, Hiroshi Motoda:
Extracting influential nodes on a social network for information diffusion. Data Min. Knowl. Discov. 20(1): 70-97 (2010) - [j17]Daisuke Kitakoshi
, Hiroyuki Shioya, Ryohei Nakano:
Empirical analysis of an on-line adaptive system using a mixture of Bayesian networks. Inf. Sci. 180(15): 2856-2874 (2010) - [j16]Yuta Ishikawa, Ichiro Takeuchi, Ryohei Nakano:
Multi-directional search from the primitive initial point for Gaussian mixture estimation using variational Bayes method. Neural Networks 23(3): 356-364 (2010) - [c65]Ryohei Nakano, Naoya Yamada:
Number Theory-based Induction of Deterministic Context-free L-system Grammar. KDIR 2010: 194-199 - [c64]Yusuke Tanahashi, Ryohei Nakano, Kazumi Saito:
Nominally Conditioned Linear Regression. ICANN (3) 2010: 290-293
2000 – 2009
- 2009
- [j15]Masayuki Karasuyama, Ichiro Takeuchi, Ryohei Nakano:
Efficient Leave-m-out Cross-Validation of Support Vector Regression by Generalizing Decremental Algorithm. New Gener. Comput. 27(4): 307-318 (2009) - [c63]Yusuke Tanahashi, Ryohei Nakano:
Bidirectional Clustering of MLP Weights for Finding Nominally Conditioned Polynomials. ICANN (2) 2009: 155-164 - [c62]Yuta Ishikawa, Ichiro Takeuchi, Ryohei Nakano:
Variational Bayes from the Primitive Initial Point for Gaussian Mixture Estimation. ICONIP (1) 2009: 159-166 - [c61]Naoyuki Harada, Yuta Ishikawa, Ichiro Takeuchi, Ryohei Nakano:
A Bayesian Graph Clustering Approach Using the Prior Based on Degree Distribution. ICONIP (1) 2009: 167-174 - 2008
- [c60]Masayuki Karasuyama, Ryohei Nakano:
Optimizing Sparse Kernel Ridge Regression hyperparameters based on leave-one-out cross-validation. IJCNN 2008: 3463-3468 - [c59]Yuta Ishikawa, Ryohei Nakano:
EM Algorithm with PIP Initialization and Temperature-Based Selection. KES (3) 2008: 58-66 - [c58]Kazumi Saito, Ryohei Nakano, Masahiro Kimura:
Prediction of Information Diffusion Probabilities for Independent Cascade Model. KES (3) 2008: 67-75 - [c57]Masayuki Karasuyama, Ichiro Takeuchi, Ryohei Nakano:
Reducing SVR Support Vectors by Using Backward Deletion. KES (3) 2008: 76-83 - 2007
- [j14]Kazumi Saito, Ryohei Nakano:
Bidirectional clustering of weights for neural networks with common weights. Syst. Comput. Jpn. 38(10): 46-57 (2007) - [c56]Masahiro Kimura, Kazumi Saito, Ryohei Nakano:
Extracting Influential Nodes for Information Diffusion on a Social Network. AAAI 2007: 1371-1376 - [c55]Yuta Ishikawa, Ryohei Nakano:
Obtaining EM Initial Points by Using the Primitive Initial Point and Subsampling Strategy. IJCNN 2007: 1115-1120 - [c54]Masayuki Karasuyama, Ryohei Nakano:
Optimizing SVR Hyperparameters via Fast Cross-Validation using AOSVR. IJCNN 2007: 1186-1191 - [c53]Ying Yan, Yusuke Tanahashi, Ryohei Nakano:
A Set of Linear Regressions with Automatic Nominal Space Partition Using a Four-Layer Perceptron. IMECS 2007: 53-58 - [c52]Kosuke Inagaki, Ryohei Nakano:
Learning Evaluation Functions of Shogi Positions from Different Sets of Games. KES (3) 2007: 210-217 - [c51]Yusuke Tanahashi, Daisuke Kitakoshi, Ryohei Nakano:
Nominally Piecewise Multiple Regression Using a Four-Layer Perceptron. KES (3) 2007: 218-226 - [c50]Kazumi Saito, Ryohei Nakano, Masahiro Kimura:
Prediction of Link Attachments by Estimating Probabilities of Information Propagation. KES (3) 2007: 235-242 - 2006
- [c49]Masayuki Karasuyama, Daisuke Kitakoshi, Ryohei Nakano:
Revised Optimizer of SVR Hyperparameters Minimizing Cross-Validation Error. IJCNN 2006: 319-326 - [c48]Yuta Ishikawa, Ryohei Nakano:
Landscape of a Likelihood Surface for a Gaussian Mixture and its use for the EM Algorithm. IJCNN 2006: 1434-1440 - [c47]Kazumi Saito, Ryohei Nakano:
Improving Convergence Performance of PageRank Computation Based on Step-Length Calculation Approach. KES (2) 2006: 945-952 - [c46]Yusuke Tanahashi, Kazumi Saito, Daisuke Kitakoshi, Ryohei Nakano:
Finding Nominally Conditioned Multivariate Polynomials Using a Four-Layer Perceptron Having Shared Weights. KES (2) 2006: 969-976 - 2005
- [c45]Kazumi Saito, Ryohei Nakano:
Weight sharing on naive Bayes document model. IJCNN 2005: 576-581 - [c44]Kenji Kobayashi, Daisuke Kitakoshi, Ryohei Nakano:
Yet faster method to optimize SVR hyperparameters based on minimizing cross-validation error. IJCNN 2005: 871-876 - [c43]Yusuke Tanahashi, Xiang-Fang Chin, Kazumi Saito, Ryohei Nakano:
Finding a succinct multi-layer perceptron having shared weights. IJCNN 2005: 1418-1423 - [c42]Yusuke Tanahashi, Kazumi Saito, Ryohei Nakano:
Model Selection and Weight Sharing of Multi-layer Perceptrons. KES (4) 2005: 716-722 - [c41]Daisuke Kitakoshi, Hiroyuki Shioya, Ryohei Nakano:
Analysis for Adaptability of Policy-Improving System with a Mixture Model of Bayesian Networks to Dynamic Environments. KES (4) 2005: 730-737 - 2004
- [c40]Tetsuro Kawai, Ryohei Nakano:
Threshold-based multi-thread EM algorithm. IJCNN 2004: 1051-1056 - [c39]Kazumi Saito, Ryohei Nakano:
Extracting characteristic words of text using neural networks. IJCNN 2004: 1397-1402 - [c38]Yusuke Tanahashi, Kazumi Saito, Ryohei Nakano:
Piecewise Multivariate Polynomials Using a Four-Layer Perceptron. KES 2004: 602-608 - [c37]Satoshi Tanimoto, Ryohei Nakano:
Learning an Evaluation Function for Shogi from Data of Games. KES 2004: 609-615 - 2002
- [j13]Kazumi Saito, Ryohei Nakano:
Extracting regression rules from neural networks. Neural Networks 15(10): 1279-1288 (2002) - [c36]Kazumi Saito, Ryohei Nakano:
Structuring Neural Networks through Bidirectional Clustering of Weights. Discovery Science 2002: 206-219 - [c35]Ryohei Nakano, Kazumi Saito:
Discovering Polynomials to Fit Multivariate Data Having Numeric and Nominal Variables. Progress in Discovery Science 2002: 482-493 - 2001
- [c34]Ryohei Nakano, Kazumi Saito:
Finding Polynomials to Fit Multivariate Data Having Numeric and Nominal Variables. IDA 2001: 258-267 - 2000
- [j12]Kazumi Saito, Ryohei Nakano:
Second-Order Learning Algorithm with Squared Penalty Term. Neural Comput. 12(3): 709-729 (2000) - [j11]Naonori Ueda, Ryohei Nakano, Zoubin Ghahramani, Geoffrey E. Hinton:
SMEM Algorithm for Mixture Models. Neural Comput. 12(9): 2109-2128 (2000) - [j10]Ken-ichi Arai, Ryohei Nakano:
Stable behavior in a recurrent neural network for a finite state machine. Neural Networks 13(6): 667-680 (2000) - [j9]Masahiro Kimura, Ryohei Nakano:
Dynamical systems produced by recurrent neural networks. Syst. Comput. Jpn. 31(4): 77-86 (2000) - [j8]Naonori Ueda, Ryohei Nakano:
EM algorithm with split and merge operations for mixture models. Syst. Comput. Jpn. 31(5): 1-11 (2000) - [j7]Naonori Ueda, Ryohei Nakano, Zoubin Ghahramani, Geoffrey E. Hinton:
Split and Merge EM Algorithm for Improving Gaussian Mixture Density Estimates. J. VLSI Signal Process. 26(1-2): 133-140 (2000) - [c33]Kazumi Saito, Ryohei Nakano:
Discovery of Nominally Conditioned Polynomials Using Neural Networks, Vector Quantizers and Decision Trees. Discovery Science 2000: 325-329 - [c32]Kazumi Saito, Ryohei Nakano:
Discovery of Relevant Weights by Minimizing Cross-Validation Error. PAKDD 2000: 372-375
1990 – 1999
- 1999
- [c31]Ryohei Nakano, Kazumi Saito:
Discovery of a Set of Nominally Conditioned Polynomials. Discovery Science 1999: 287-298 - 1998
- [j6]Naonori Ueda, Ryohei Nakano:
Deterministic annealing EM algorithm. Neural Networks 11(2): 271-282 (1998) - [j5]Masahiro Kimura, Ryohei Nakano:
Learning dynamical systems by recurrent neural networks from orbits. Neural Networks 11(9): 1589-1599 (1998) - [c30]Ryohei Nakano, Kazumi Saito:
Computational Characteristics of Law Discovery Using Neural Networks. Discovery Science 1998: 342-351 - [c29]Naonori Ueda, Ryohei Nakano, Zoubin Ghahramani, Geoffrey E. Hinton:
SMEM Algorithm for Mixture Models. NIPS 1998: 599-605 - [c28]Takeshi Yamada, Kazuyuki Yoshimura, Ryohei Nakano:
Information Operator Scheduling by Genetic Algorithms. SEAL 1998: 50-57 - 1997
- [j4]Kazumi Saito, Ryohei Nakano:
Partial BFGS Update and Efficient Step-Length Calculation for Three-Layer Neural Networks. Neural Comput. 9(1): 123-141 (1997) - [c27]Masahiro Kimura, Ryohei Nakano:
Unique Representations of Dynamical Systems Produced by Recurrent Neural Networks. ICANN 1997: 403-408 - [c26]Kazumi Saito, Ryohei Nakano:
MDL regularizer: a new regularizer based on the MDL principle. ICNN 1997: 1833-1838 - [c25]Kenichi Arai, Ryohei Nakano:
Adaptive β Scheduling Learning Method of Finite State Automata by Recurrent Neural Networks. ICONIP (1) 1997: 351-354 - [c24]Kazumi Saito, Ryohei Nakano:
Numeric Law Discovery Using Neural Networks. ICONIP (2) 1997: 843-846 - [c23]Kazumi Saito, Ryohei Nakano:
Law Discovery using Neural Networks. IJCAI 1997: 1078-1083 - 1996
- [c22]Masahiro Kimura, Ryohei Nakano:
Learning Dynamical Systems Produced by Recurrent Neural Networks. ICANN 1996: 133-138 - [c21]Ken-ichi Arai, Ryohei Nakano:
Annealed RNN Learning of Finite State Automata. ICANN 1996: 519-524 - [c20]Naonori Ueda, Ryohei Nakano:
Generalization error of ensemble estimators. ICNN 1996: 90-95 - [c19]Kazumi Saito, Ryohei Nakano:
A constructive learning algorithm for an HME. ICNN 1996: 1268-1273 - [c18]Kazumi Saito, Ryohei Nakano:
Second-order Learning Algorithm with Squared Penalty Term. NIPS 1996: 627-633 - [c17]Takeshi Yamada, Ryohei Nakano:
Scheduling by Genetic Local Search with Multi-Step Crossover. PPSN 1996: 960-969 - 1995
- [j3]Naonori Ueda, Ryohei Nakano:
Competitive and selective learning method for vector quantizer design - Equidistortion principle and its algorithm. Syst. Comput. Jpn. 26(9): 34-49 (1995) - [c16]Naonori Ueda, Ryohei Nakano:
Estimating expected error rates of neural network classifiers in small sample size situations: a comparison of cross-validation and bootstrap. ICNN 1995: 101-104 - [c15]Pablo A. Estévez, Ryohei Nakano:
Hierarchical mixture of experts and Max-Min propagation neural networks. ICNN 1995: 651-656 - [c14]Ryohei Nakano, Naonori Ueda, Kazumi Saito, Takeshi Yamada:
Parrot-like speaking using optimal vector quantization. ICNN 1995: 2871-2875 - [c13]Masahiro Kimura, Ryohei Nakano:
Learning Dynamical Systems from Trajectories by Continuous Time Recurrent Neural Networks. ICNN 1995: 2992-2997 - [c12]Kazumi Saito, Ryohei Nakano:
A Connectionist Approach to Numeric Law Discorvery. Machine Intelligence 15 1995: 315-327 - 1994
- [j2]Naonori Ueda, Ryohei Nakano:
A new competitive learning approach based on an equidistortion principle for designing optimal vector quantizers. Neural Networks 7(8): 1211-1227 (1994) - [c11]Kazumi Saito, Ryohei Nakano:
Adaptive Concept Learning Algorithm. IFIP Congress (1) 1994: 294-299 - [c10]Naonori Ueda, Ryohei Nakano:
Deterministic Annealing Variant of the EM Algorithm. NIPS 1994: 545-552 - [c9]Ryohei Nakano, Yuval Davidor, Takeshi Yamada:
Optimal Population Size under Constant Computation Cost. PPSN 1994: 130-138 - 1993
- [c8]Yuval Davidor, Takeshi Yamada, Ryohei Nakano:
The ECOlogical Framework II : Improving GA Performance At Virtually Zero Cost. ICGA 1993: 171-176 - [c7]Naonori Ueda, Ryohei Nakano:
A competitive and selective learning method for designing optimal vector quantizers. ICNN 1993: 1444-1450 - [c6]Kazumi Saito, Ryohei Nakano:
A concept learning algorithm with adaptive search. Machine Intelligence 14 1993: 353- - 1992
- [c5]Takeshi Yamada, Ryohei Nakano:
A Genetic Algorithm Applicable to Large-Scale Job-Shop Problems. PPSN 1992: 283-292 - 1991
- [c4]Ryohei Nakano, Takeshi Yamada:
Conventional Genetic Algorithm for Job Shop Problems. ICGA 1991: 474-479 - 1990
- [j1]Ryohei Nakano:
Translation with Optimization from Relational Calculus to Relational Algebra Having Aggregate Functions. ACM Trans. Database Syst. 15(4): 518-557 (1990)
1980 – 1989
- 1988
- [c3]Kazumi Saito, Ryohei Nakano:
Medical diagnostic expert system based on PDP model. ICNN 1988: 255-262 - 1987
- [c2]Ryohei Nakano, Minoru Kiyama:
MACH: Much Faster Associative Machine. IWDM 1987: 339-352 - 1983
- [c1]Ryohei Nakano:
Integrity Checking in a Logic-Oriented ER Model. ER 1983: 551-564
Coauthor Index
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