Integrity Checking in a Logic-Oriented ER Model. - Ryohei Nakano (ER, 1983)

MACH: Much Faster Associative Machine. - Ryohei Nakano; Minoru Kiyama (IWDM, 1987)

Medical diagnostic expert system based on PDP model. - Kazumi Saito; Ryohei Nakano (ICNN, 1988)

Translation with Optimization from Relational Calculus to Relational Algebra Having Aggregate Functions. - Ryohei Nakano (ACM Trans. Database Syst., 1990)

Conventional Genetic Algorithm for Job Shop Problems. - Ryohei Nakano; Takeshi Yamada (ICGA, 1991)

A Genetic Algorithm Applicable to Large-Scale Job-Shop Problems. - Takeshi Yamada; Ryohei Nakano (PPSN, 1992)

The ECOlogical Framework II : Improving GA Performance At Virtually Zero Cost. - Yuval Davidor; Takeshi Yamada; Ryohei Nakano (ICGA, 1993)

A competitive and selective learning method for designing optimal vector quantizers. - Naonori Ueda; Ryohei Nakano (ICNN, 1993)

A concept learning algorithm with adaptive search. - Kazumi Saito; Ryohei Nakano (Machine Intelligence 14, 1993)

Adaptive Concept Learning Algorithm. - Kazumi Saito; Ryohei Nakano (IFIP Congress (1), 1994)

Deterministic Annealing Variant of the EM Algorithm. - Naonori Ueda; Ryohei Nakano (NIPS, 1994)

Optimal Population Size under Constant Computation Cost. - Ryohei Nakano; Yuval Davidor; Takeshi Yamada (PPSN, 1994)

A new competitive learning approach based on an equidistortion principle for designing optimal vector quantizers. - Naonori Ueda; Ryohei Nakano (Neural Networks, 1994)

Hierarchical mixture of experts and Max-Min propagation neural networks. - Pablo A. Estévez; Ryohei Nakano (ICNN, 1995)

Learning Dynamical Systems from Trajectories by Continuous Time Recurrent Neural Networks. - Masahiro Kimura; Ryohei Nakano (ICNN, 1995)

Parrot-like speaking using optimal vector quantization. - Ryohei Nakano; Naonori Ueda; Kazumi Saito; Takeshi Yamada (ICNN, 1995)

Estimating expected error rates of neural network classifiers in small sample size situations: a comparison of cross-validation and bootstrap. - Naonori Ueda; Ryohei Nakano (ICNN, 1995)

A Connectionist Approach to Numeric Law Discorvery. - Kazumi Saito; Ryohei Nakano (Machine Intelligence 15, 1995)

Competitive and selective learning method for vector quantizer design - Equidistortion principle and its algorithm. - Naonori Ueda; Ryohei Nakano (Syst. Comput. Jpn., 1995)

Annealed RNN Learning of Finite State Automata. - Ken-ichi Arai; Ryohei Nakano (ICANN, 1996)

Learning Dynamical Systems Produced by Recurrent Neural Networks. - Masahiro Kimura; Ryohei Nakano (ICANN, 1996)

A constructive learning algorithm for an HME. - Kazumi Saito; Ryohei Nakano (ICNN, 1996)

Generalization error of ensemble estimators. - Naonori Ueda; Ryohei Nakano (ICNN, 1996)

Second-order Learning Algorithm with Squared Penalty Term. - Kazumi Saito; Ryohei Nakano (NIPS, 1996)

Scheduling by Genetic Local Search with Multi-Step Crossover. - Takeshi Yamada; Ryohei Nakano (PPSN, 1996)

Unique Representations of Dynamical Systems Produced by Recurrent Neural Networks. - Masahiro Kimura; Ryohei Nakano (ICANN, 1997)

MDL regularizer: a new regularizer based on the MDL principle. - Kazumi Saito; Ryohei Nakano (ICNN, 1997)

Adaptive β Scheduling Learning Method of Finite State Automata by Recurrent Neural Networks. - Kenichi Arai; Ryohei Nakano (ICONIP (1), 1997)

Numeric Law Discovery Using Neural Networks. - Kazumi Saito; Ryohei Nakano (ICONIP (2), 1997)

Law Discovery using Neural Networks. - Kazumi Saito; Ryohei Nakano (IJCAI, 1997)

Partial BFGS Update and Efficient Step-Length Calculation for Three-Layer Neural Networks. - Kazumi Saito; Ryohei Nakano (Neural Comput., 1997)

Computational Characteristics of Law Discovery Using Neural Networks. - Ryohei Nakano; Kazumi Saito (Discovery Science, 1998)

SMEM Algorithm for Mixture Models. - Naonori Ueda; Ryohei Nakano; Zoubin Ghahramani; Geoffrey E. Hinton (NIPS, 1998)

Information Operator Scheduling by Genetic Algorithms. - Takeshi Yamada; Kazuyuki Yoshimura; Ryohei Nakano (SEAL, 1998)

Learning dynamical systems by recurrent neural networks from orbits. - Masahiro Kimura; Ryohei Nakano (Neural Networks, 1998)

Deterministic annealing EM algorithm. - Naonori Ueda; Ryohei Nakano (Neural Networks, 1998)

Discovery of a Set of Nominally Conditioned Polynomials. - Ryohei Nakano; Kazumi Saito (Discovery Science, 1999)

Discovery of Nominally Conditioned Polynomials Using Neural Networks, Vector Quantizers and Decision Trees. - Kazumi Saito; Ryohei Nakano (Discovery Science, 2000)

Discovery of Relevant Weights by Minimizing Cross-Validation Error. - Kazumi Saito; Ryohei Nakano (PAKDD, 2000)

Stable behavior in a recurrent neural network for a finite state machine. - Ken-ichi Arai; Ryohei Nakano (Neural Networks, 2000)

Dynamical systems produced by recurrent neural networks. - Masahiro Kimura; Ryohei Nakano (Syst. Comput. Jpn., 2000)

EM algorithm with split and merge operations for mixture models. - Naonori Ueda; Ryohei Nakano (Syst. Comput. Jpn., 2000)

Split and Merge EM Algorithm for Improving Gaussian Mixture Density Estimates. - Naonori Ueda; Ryohei Nakano; Zoubin Ghahramani; Geoffrey E. Hinton (J. VLSI Signal Process., 2000)

Finding Polynomials to Fit Multivariate Data Having Numeric and Nominal Variables. - Ryohei Nakano; Kazumi Saito (IDA, 2001)

Structuring Neural Networks through Bidirectional Clustering of Weights. - Kazumi Saito; Ryohei Nakano (Discovery Science, 2002)

Discovering Polynomials to Fit Multivariate Data Having Numeric and Nominal Variables. - Ryohei Nakano; Kazumi Saito (Progress in Discovery Science, 2002)

Extracting regression rules from neural networks. - Kazumi Saito; Ryohei Nakano (Neural Networks, 2002)

Optimizing Support Vector regression hyperparameters based on cross-validation. - Kentaro Ito; Ryohei Nakano (IJCNN, 2003)

Threshold-based dynamic annealing for multi-thread DAEM and its extreme. - Masaharu Takada; Ryohei Nakano (IJCNN, 2003)

Threshold-based multi-thread EM algorithm. - Tetsuro Kawai; Ryohei Nakano (IJCNN, 2004)

Extracting characteristic words of text using neural networks. - Kazumi Saito; Ryohei Nakano (IJCNN, 2004)

Piecewise Multivariate Polynomials Using a Four-Layer Perceptron. - Yusuke Tanahashi; Kazumi Saito; Ryohei Nakano (KES, 2004)

Learning an Evaluation Function for Shogi from Data of Games. - Satoshi Tanimoto; Ryohei Nakano (KES, 2004)

Yet faster method to optimize SVR hyperparameters based on minimizing cross-validation error. - Kenji Kobayashi; Daisuke Kitakoshi; Ryohei Nakano (IJCNN, 2005)

Weight sharing on naive Bayes document model. - Kazumi Saito; Ryohei Nakano (IJCNN, 2005)

Finding a succinct multi-layer perceptron having shared weights. - Yusuke Tanahashi; Xiang-Fang Chin; Kazumi Saito; Ryohei Nakano (IJCNN, 2005)

Analysis for Adaptability of Policy-Improving System with a Mixture Model of Bayesian Networks to Dynamic Environments. - Daisuke Kitakoshi; Hiroyuki Shioya; Ryohei Nakano (KES (4), 2005)

Model Selection and Weight Sharing of Multi-layer Perceptrons. - Yusuke Tanahashi; Kazumi Saito; Ryohei Nakano (KES (4), 2005)

Landscape of a Likelihood Surface for a Gaussian Mixture and its use for the EM Algorithm. - Yuta Ishikawa; Ryohei Nakano (IJCNN, 2006)

Revised Optimizer of SVR Hyperparameters Minimizing Cross-Validation Error. - Masayuki Karasuyama; Daisuke Kitakoshi; Ryohei Nakano (IJCNN, 2006)

Improving Convergence Performance of PageRank Computation Based on Step-Length Calculation Approach. - Kazumi Saito; Ryohei Nakano (KES (2), 2006)

Finding Nominally Conditioned Multivariate Polynomials Using a Four-Layer Perceptron Having Shared Weights. - Yusuke Tanahashi; Kazumi Saito; Daisuke Kitakoshi; Ryohei Nakano (KES (2), 2006)

Extracting Influential Nodes for Information Diffusion on a Social Network. - Masahiro Kimura; Kazumi Saito; Ryohei Nakano (AAAI, 2007)

Obtaining EM Initial Points by Using the Primitive Initial Point and Subsampling Strategy. - Yuta Ishikawa; Ryohei Nakano (IJCNN, 2007)

Optimizing SVR Hyperparameters via Fast Cross-Validation using AOSVR. - Masayuki Karasuyama; Ryohei Nakano (IJCNN, 2007)

A Set of Linear Regressions with Automatic Nominal Space Partition Using a Four-Layer Perceptron. - Ying Yan; Yusuke Tanahashi; Ryohei Nakano (IMECS, 2007)

Learning Evaluation Functions of Shogi Positions from Different Sets of Games. - Kosuke Inagaki; Ryohei Nakano (KES (3), 2007)

Prediction of Link Attachments by Estimating Probabilities of Information Propagation. - Kazumi Saito; Ryohei Nakano; Masahiro Kimura (KES (3), 2007)

Nominally Piecewise Multiple Regression Using a Four-Layer Perceptron. - Yusuke Tanahashi; Daisuke Kitakoshi; Ryohei Nakano (KES (3), 2007)

Bidirectional clustering of weights for neural networks with common weights. - Kazumi Saito; Ryohei Nakano (Syst. Comput. Jpn., 2007)

Optimizing Sparse Kernel Ridge Regression hyperparameters based on leave-one-out cross-validation. - Masayuki Karasuyama; Ryohei Nakano (IJCNN, 2008)

EM Algorithm with PIP Initialization and Temperature-Based Selection. - Yuta Ishikawa; Ryohei Nakano (KES (3), 2008)

Reducing SVR Support Vectors by Using Backward Deletion. - Masayuki Karasuyama; Ichiro Takeuchi; Ryohei Nakano (KES (3), 2008)

Prediction of Information Diffusion Probabilities for Independent Cascade Model. - Kazumi Saito; Ryohei Nakano; Masahiro Kimura (KES (3), 2008)

Bidirectional Clustering of MLP Weights for Finding Nominally Conditioned Polynomials. - Yusuke Tanahashi; Ryohei Nakano (ICANN (2), 2009)

A Bayesian Graph Clustering Approach Using the Prior Based on Degree Distribution. - Naoyuki Harada; Yuta Ishikawa; Ichiro Takeuchi; Ryohei Nakano (ICONIP (1), 2009)

Variational Bayes from the Primitive Initial Point for Gaussian Mixture Estimation. - Yuta Ishikawa; Ichiro Takeuchi; Ryohei Nakano (ICONIP (1), 2009)

Efficient Leave-m-out Cross-Validation of Support Vector Regression by Generalizing Decremental Algorithm. - Masayuki Karasuyama; Ichiro Takeuchi; Ryohei Nakano (New Gener. Comput., 2009)

Number Theory-based Induction of Deterministic Context-free L-system Grammar. - Ryohei Nakano; Naoya Yamada (KDIR, 2010)

Nominally Conditioned Linear Regression. - Yusuke Tanahashi; Ryohei Nakano; Kazumi Saito (ICANN (3), 2010)

Extracting influential nodes on a social network for information diffusion. - Masahiro Kimura; Kazumi Saito; Ryohei Nakano; Hiroshi Motoda (Data Min. Knowl. Discov., 2010)

Empirical analysis of an on-line adaptive system using a mixture of Bayesian networks. - Daisuke Kitakoshi; Hiroyuki Shioya; Ryohei Nakano (Inf. Sci., 2010)

Multi-directional search from the primitive initial point for Gaussian mixture estimation using variational Bayes method. - Yuta Ishikawa; Ichiro Takeuchi; Ryohei Nakano (Neural Networks, 2010)

Learning Method Utilizing Singular Region of Multilayer Perceptron. - Ryohei Nakano; Seiya Satoh; Takayuki Ohwaki (IJCCI (NCTA), 2011)

Complex-Valued Multilayer Perceptron Search Utilizing Eigen Vector Descent and Reducibility Mapping. - Shinya Suzumura; Ryohei Nakano (ICANN (2), 2012)

Emergent Induction of Deterministic Context-Free L-system Grammar. - Ryohei Nakano (IBICA, 2013)

Fast and Stable Learning Utilizing Singular Regions of Multilayer Perceptron. - Seiya Satoh; Ryohei Nakano (Neural Process. Lett., 2013)

Emergent Induction of L-system Grammar from a String with Deletion-type Transmutation. - Ryohei Nakano (KDIR, 2014)

Complex-Valued Multilayer Perceptron Search Utilizing Singular Regions of Complex-Valued Parameter Space. - Seiya Satoh; Ryohei Nakano (ICANN, 2014)

Singularity Stairs Following with Limited Numbers of Hidden Units. - Seiya Satoh; Ryohei Nakano (IJCCI (NCTA), 2014)

A Yet Faster Version of Complex-valued Multilayer Perceptron Learning using Singular Regions and Search Pruning. - Seiya Satoh; Ryohei Nakano (IJCCI (NCTA), 2015)

Complex-valued multilayer perceptron learning using singular regions and search pruning. - Seiya Satoh; Ryohei Nakano (IJCNN, 2015)

How complex-valued multilayer perceptron can predict the behavior of deterministic chaos. - Seiya Satoh; Ryohei Nakano (IJCNN, 2016)

How New Information Criteria WAIC and WBIC Worked for MLP Model Selection. - Seiya Satoh; Ryohei Nakano (ICPRAM, 2017)

Performance of Complex-Valued Multilayer Perceptrons Largely Depends on Learning Methods. - Seiya Satoh; Ryohei Nakano (IJCCI, 2017)

A New Method for Learning RBF Networks by Utilizing Singular Regions. - Seiya Satoh; Ryohei Nakano (ICAISC (1), 2018)

Mixture of Multilayer Perceptron Regressions. - Ryohei Nakano; Seiya Satoh (ICPRAM, 2019)

Faster RBF Network Learning Utilizing Singular Regions. - Seiya Satoh; Ryohei Nakano (ICPRAM, 2019)