Ryotaro Kamimura

189 papers B 45C 18Journal 59Unranked 67

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Detecting Internal and External Simplification by Min-Max Potentiality Control for Interpreting Multi-layered Neural Networks. SN Comput. Sci. Ryotaro Kamimura
2025	—	`conf`	Simplification Principle for Adding Complexity in Multi-Layered Neural Networks. IIAI-AAI-Winter Ryotaro Kamimura
2024	—	`conf`	Forced and Natural Creative-Prototype Learning for Interpreting Multi-Layered Neural Networks. IIAI-AAI Ryotaro Kamimura
2024	C	`conf`	Searching for Idealized Prototype Learning for Interpreting Multi-Layered Neural Networks. IJCCI Ryotaro Kamimura
2023	J	`jnl`	Contradiction neutralization for interpreting multi-layered neural networks. Appl. Intell. Ryotaro Kamimura
2023	J	`jnl`	Destructive computing with winner-lose-all competition in multi-layered neural networks. Int. J. Hybrid Intell. Syst. Ryotaro Kamimura
2023	J	`jnl`	Impartial competitive learning in multi-layered neural networks. Connect. Sci. Ryotaro Kamimura
2023	—	`conf`	Repeated Potentiality Augmentation for Multi-layered Neural Networks. FICC (2) Ryotaro Kamimura
2022	J	`jnl`	Cost-forced and repeated selective information minimization and maximization for multi-layered neural networks. Int. J. Hybrid Intell. Syst. Ryotaro Kamimura
2022	J	`jnl`	Cost-forced collective potentiality maximization by complementary potentiality minimization for interpreting multi-layered neural networks. Neurocomputing Ryotaro Kamimura
2022	—	`conf`	Min-Max Cost and Information Control in Multi-layered Neural Networks. FTC (1) Ryotaro Kamimura, Ryozo Kitajima
2022	J	`jnl`	Multi-level selective potentiality maximization for interpreting multi-layered neural networks. Appl. Intell. Ryotaro Kamimura
2022	C	`conf`	Serially Disentangled Learning for Multi-Layered Neural Networks. IEA/AIE Ryotaro Kamimura, Ryozo Kitajima
2021	—	`conf`	Forced Selective Information Reduction for Interpreting Multi-Layered Neural Networks. IIAI-AAI-Winter Ryotaro Kamimura, Ryozo Kitajima
2021	J	`jnl`	Partially black-boxed collective interpretation and its application to SOM-based convolutional neural networks. Neurocomputing Ryotaro Kamimura
2021	C	`conf`	Selective Information Control and Layer-Wise Partial Collective Compression for Multi-Layered Neural Networks. ISDA Ryotaro Kamimura
2021	—	`conf`	Selective Information Control and Network Compression in Multi-layered Neural Networks. IntelliSys (1) Ryotaro Kamimura
2020	—	`conf`	Bidirectional Estimation of Partially Black-Boxed Layers of SOM-Based Convolutional Neural Networks. IntelliSys (1) Ryotaro Kamimura
2020	C	`conf`	Compressing and Interpreting SOM-Based Convolutional Neural Networks. IEA/AIE Ryotaro Kamimura
2020	—	`conf`	Connective Potential Information for Collectively Interpreting Multi-Layered Neural Networks. SSCI Ryotaro Kamimura
2020	C	`conf`	Cost-Conscious Internal Information Maximization for Disentangling and Interpreting Multi-layered Neural Networks. HIS Ryotaro Kamimura
2020	J	`jnl`	Cost-conscious mutual information maximization for improving collective interpretation of multi-layered neural networks. Neurocomputing Ryotaro Kamimura
2020	—	`conf`	Disentangled Representations by Pseudo-Maximum Mutual Information for Interpreting Multi-Layered Neural Networks. IIAI-AAI Ryotaro Kamimura
2020	J	`jnl`	Improving collective interpretation by extended potentiality assimilation for multi-layered neural networks. Connect. Sci. Ryotaro Kamimura, Haruhiko Takeuchi
2020	J	`jnl`	Minimum interpretation by autoencoder-based serial and enhanced mutual information production. Appl. Intell. Ryotaro Kamimura
2019	—	`conf`	Information Compression for Intelligible Multi-Layered Neural Networks. IIAI-AAI Ryotaro Kamimura
2019	—	`conf`	Interpreting Collectively Compressed Multi-Layered Neural Networks. CIS/RAM Ryotaro Kamimura
2019	B	`conf`	Mutual Information Generation for Improving Generalization and Interpretation in Neural Networks. IJCNN Ryotaro Kamimura
2019	J	`jnl`	Neural self-compressor: Collective interpretation by compressing multi-layered neural networks into non-layered networks. Neurocomputing Ryotaro Kamimura
2019	—	`conf`	Relationship Between Management Policies and Profitability for Second Section-Listed Manufacturing Companies of the Tokyo Stock Exchange (2016 Results). IIAI-AAI Ryozo Kitajima, Ryotaro Kamimura, Hiroyuki Sakai
2019	J	`jnl`	SOM-based information maximization to improve and interpret multi-layered neural networks: From information reduction to information augmentation approach to create new information. Expert Syst. Appl. Ryotaro Kamimura
2019	J	`jnl`	Sparse semi-autoencoders to solve the vanishing information problem in multi-layered neural networks. Appl. Intell. Ryotaro Kamimura, Haruhiko Takeuchi
2019	J	`jnl`	Supposed Maximum Mutual Information for Improving Generalization and Interpretation of Multi-Layered Neural Networks. J. Artif. Intell. Soft Comput. Res. Ryotaro Kamimura
2018	—	`conf`	Autoeconder-Based Excessive Information Generation for Improving and Interpreting Multi-layered Neural Networks. IIAI-AAI Ryotaro Kamimura, Haruhiko Takeuchi
2018	—	`conf`	Autoeoncoders and Information Augmentation for Improved Generalization and Interpretation in Multi-layered Neural Networks. ISCBI Ryotaro Kamimura
2018	—	`conf`	Correlation-Constrained Mutual Information Maximization for Interpretable Multi-Layered Neural Networks. SCIS&ISIS Ryotaro Kamimura, Haruhiko Takeuchi
2018	—	`conf`	Excessive, Selective and Collective Information Processing to Improve and Interpret Multi-layered Neural Networks. IntelliSys (1) Ryotaro Kamimura, Haruhiko Takeuchi
2018	—	`conf`	Information-Theoretic Self-compression of Multi-layered Neural Networks. TPNC Ryotaro Kamimura
2018	—	`conf`	Local Selective Learning for Interpreting Multi-Layered Neural Networks. SCIS&ISIS Ryotaro Kamimura, Ryozo Kitajima, Hiroyuki Sakai
2018	J	`jnl`	Self-Assimilation for Solving Excessive Information Acquisition in Potential Learning. J. Artif. Intell. Soft Comput. Res. Ryotaro Kamimura, Tsubasa Kitago
2017	J	`jnl`	Collective mutual information maximization to unify passive and positive approaches for improving interpretation and generalization. Neural Networks Ryotaro Kamimura
2017	—	`conf`	Direct Potentiality Assimilation for Improving Multi-Layered Neural Networks. FedCSIS (Position Papers) Ryotaro Kamimura
2017	—	`conf`	Mutual information maximization for improving and interpreting multi-layered neural networks. SSCI Ryotaro Kamimura
2017	B	`conf`	Potential layer-wise supervised learning for training multi-layered neural networks. IJCNN Ryotaro Kamimura
2017	B	`conf`	Selective and cooperative potentiality maximization for improving interpretation and generalization. IJCNN Ryotaro Kamimura
2017	—	`conf`	Supervised semi-autoencoder learning for multi-layered neural networks. IFSA-SCIS Ryotaro Kamimura, Haruhiko Takeuchi
2016	—	`conf`	Collective Interpretation and Potential Joint Information Maximization. IIP Ryotaro Kamimura
2016	J	`jnl`	Identifying Important Tweets by Considering the Potentiality of Neurons. IEICE Trans. Fundam. Electron. Commun. Comput. Sci. Ryozo Kitajima, Ryotaro Kamimura, Osamu Uchida, Fujio Toriumi
2016	B	`conf`	Repeated potentiality assimilation: Simplifying learning procedures by positive, independent and indirect operation for improving generalization and interpretation. IJCNN Ryotaro Kamimura
2016	C	`conf`	Simple and Stable Internal Representation by Potential Mutual Information Maximization. EANN Ryotaro Kamimura
2016	—	`conf`	Solving the Vanishing Information Problem with Repeated Potential Mutual Information Maximization. ICONIP (4) Ryotaro Kamimura
2015	J	`jnl`	Accumulative Information Enhancement In The Self-Organizing Maps And Its Application To The Analysis Of Mission Statements. J. Artif. Intell. Soft Comput. Res. Ryozo Kitajima, Ryotaro Kamimura
2015	J	`jnl`	Improving visualisation and prediction performance of supervised self-organising map by modified contradiction resolution. Connect. Sci. Ryotaro Kamimura
2015	—	`conf`	Neural potential learning for tweets classification and interpretation. SoCPaR Ryozo Kitajima, Ryotaro Kamimura, Osamu Uchida, Fujio Toriumi
2015	C	`conf`	Pseudo-potentiality maximization for improved interpretation and generalization in neural networks. IWCIA Ryotaro Kamimura
2015	B	`conf`	Selective potentiality maximization for input neuron selection in self-organizing maps. IJCNN Ryotaro Kamimura, Ryozo Kitajima
2015	B	`conf`	Self-Organized Mutual Information Maximization Learning for Improved Generalization Performance. SMC Ryotaro Kamimura
2015	B	`conf`	Self-Organizing Selective Potentiality Learning to Detect Important Input Neurons. SMC Ryotaro Kamimura
2015	B	`conf`	Simplified and gradual information control for improving generalization performance of multi-layered neural networks. IJCNN Ryotaro Kamimura
2014	B	`conf`	Embedded information enhancement for neuron selection in self-organizing maps. SMC Ryotaro Kamimura, Taeko Kamimura
2014	—	`conf`	Explicit knowledge extraction in information-theoretic supervised multi-layered SOM. FOCI Ryotaro Kamimura
2014	—	`conf`	Information acquisition performance by supervised information-theoretic self-organizing maps. NaBIC Ryotaro Kamimura
2014	B	`conf`	Information-theoretic multi-layered supervised self-organizing maps for improved prediction performance and explicit internal representation. SMC Ryotaro Kamimura
2014	J	`jnl`	Input information maximization for improving self-organizing maps. Appl. Intell. Ryotaro Kamimura
2014	—	`conf`	Simplified Information Acquisition Method to Improve Prediction Performance: Direct Use of Hidden Neuron Outputs and Separation of Information Acquisition and Use Phase. ANNIIP Ryotaro Kamimura
2013	B	`conf`	Contradiction Resolution with Dependent Input Neuron Selection for Self-Organizing Maps. SMC Ryotaro Kamimura
2013	B	`conf`	Contradiction resolution with explicit and limited evaluation and its application to SOM. IJCNN Ryotaro Kamimura
2013	J	`jnl`	Controlling Relations between the Individuality and Collectivity of Neurons and its Application to Self-Organizing Maps. Neural Process. Lett. Ryotaro Kamimura
2013	C	`conf`	Dependent input neuron selection in contradiction resolution. IWCIA Ryotaro Kamimura
2013	J	`jnl`	Repeated comprehensibility maximization in competitive learning. Neural Comput. Appl. Ryotaro Kamimura
2013	J	`jnl`	Similarity interaction in information-theoretic self-organizing maps. Int. J. Gen. Syst. Ryotaro Kamimura
2012	J	`jnl`	Comprehensibility maximization and humanly comprehensible representations. Int. J. Gen. Syst. Ryotaro Kamimura
2012	C	`conf`	Contradiction Resolution for Foreign Exchange Rates Estimation. IJCCI Ryotaro Kamimura
2012	B	`conf`	Contradiction resolution and its application to self-organizing maps. SMC Ryotaro Kamimura
2012	J	`jnl`	Double enhancement learning for explicit internal representations: unifying self-enhancement and information enhancement to incorporate information on input variables. Appl. Intell. Ryotaro Kamimura
2012	B	`conf`	Interaction of individually and collectively treated neurons for explicit class structure in self-organizing maps. IJCNN Ryotaro Kamimura
2012	J	`jnl`	Relative information maximization and its application to the extraction of explicit class structure in SOM. Neurocomputing Ryotaro Kamimura
2012	—	`conf`	Separation and Unification of Individuality and Collectivity and Its Application to Explicit Class Structure in Self-Organizing Maps. ICANN (2) Ryotaro Kamimura
2011	J	`jnl`	Constrained information maximization by free energy minimization. Int. J. Gen. Syst. Ryotaro Kamimura
2011	B	`conf`	Cooperation control and enhanced class structure in self-organizing maps. IJCNN Ryotaro Kamimura
2011	—	`conf`	Explicit Class Structure by Weighted Cooperative Learning. ICANN (1) Ryotaro Kamimura
2011	—	`conf`	Explicit class structure with closeness and similarity between neurons. NaBIC Ryotaro Kamimura
2011	—	`conf`	Individually and Collectively Treated Neurons and its Application to SOM . IJCCI (NCTA) Ryotaro Kamimura
2011	—	`conf`	Relative Information Maximization and Its Application to the Detection of Explicit Class Structure in SOM. FOCI Ryotaro Kamimura
2011	J	`jnl`	Selective information enhancement learning for creating interpretable representations in competitive learning. Neural Networks Ryotaro Kamimura
2011	J	`jnl`	Self-enhancement learning: target-creating learning and its application to self-organizing maps. Biol. Cybern. Ryotaro Kamimura
2011	J	`jnl`	Structural enhanced information and its application to improved visualization of self-organizing maps. Appl. Intell. Ryotaro Kamimura
2011	J	`jnl`	Supposed maximum information for comprehensible representations in SOM. Neurocomputing Ryotaro Kamimura
2010	—	`conf`	Explicit class structure produced by information-theoretic competitive and cooperative learning. NaBIC Ryotaro Kamimura
2010	—	`conf`	Generation of Comprehensible Representations by Supposed Maximum Information. ICANN (2) Ryotaro Kamimura
2010	J	`jnl`	Information enhancement for interpreting competitive learning. Int. J. Gen. Syst. Ryotaro Kamimura
2010	—	`conf`	Information-Theoretic Competitive and Cooperative Learning for Self-Organizing Maps. ICONIP (2) Ryotaro Kamimura
2010	J	`jnl`	Information-theoretic enhancement learning and its application to visualization of self-organizing maps. Neurocomputing Ryotaro Kamimura
2010	—	`conf`	Pseudo-network Growing for Gradual Interpretation of Input Patterns. ICONIP (2) Ryotaro Kamimura
2009	J	`jnl`	An information-theoretic approach to feature extraction in competitive learning. Neurocomputing Ryotaro Kamimura
2009	J	`jnl`	Enhancing and Relaxing Competitive Units for Feature Discovery. Neural Process. Lett. Ryotaro Kamimura
2009	J	`jnl`	Feature Discovery by Information Loss. J. Comput. Ryotaro Kamimura
2009	C	`conf`	Information Enhancement Learning: Local Enhanced Information to Detect the Importance of Input Variables in Competitive Learning. EANN Ryotaro Kamimura
2009	J	`jnl`	Relative Relaxation and Weighted Information Loss to Simplify and Stabilize Feature Detection. J. Adv. Comput. Intell. Intell. Informatics Ryotaro Kamimura
2009	B	`conf`	Selective enhancement learning in competitive learning. IJCNN Ryotaro Kamimura
2009	B	`conf`	Self-enhancement learning: Self-supervised and target-creating learning. IJCNN Ryotaro Kamimura
2009	B	`conf`	Self-supervised learning by information enhancement: Target-Generating and Spontaneous learning for Competitive Learning. SMC Ryotaro Kamimura
2009	B	`conf`	Structural Enhanced Information to Detect Features in Competitive Learning. SMC Ryotaro Kamimura
2008	—	`conf`	Collective Activations to Generate Self-Organizing Maps. ICONIP (2) Ryotaro Kamimura
2008	B	`conf`	Conditional information and information loss for flexible feature extraction. IJCNN Ryotaro Kamimura
2008	—	`conf`	Enhanced Visualization by Combing SOM and Mixture Models. ICONIP (2) Ryotaro Kamimura
2008	—	`conf`	Feature Detection by Structural Enhanced Information. ICONIP (2) Ryotaro Kamimura
2008	C	`conf`	Feature Discovery by Enhancement and Relaxation of Competitive Units. IDEAL Ryotaro Kamimura
2008	B	`conf`	Interpreting and improving multi-layered networks by free energy-based competitive learning. SMC Ryotaro Kamimura
2008	B	`conf`	Mutual information maximization by free energy-based competitive learning for self-organizing maps. SMC Ryotaro Kamimura
2008	—	`conf`	Partially Enhanced Competitive Learning. ICONIP (2) Ryotaro Kamimura
2007	—	`conf`	Combining Hard and Soft Competition in Information-Theoretic Learning. FOCI Ryotaro Kamimura
2007	B	`conf`	Controlled Competitive Learning: Extending Competitive Learning to Supervised Learning. IJCNN Ryotaro Kamimura
2007	B	`conf`	Forced Information Maximization to Accelerate Information-Theoretic Competitive Learning. IJCNN Ryotaro Kamimura, Ryozo Kitajima
2007	—	`conf`	Forced Information and Information Loss for a Student Survey Analysis. FOCI Ryotaro Kamimura
2007	—	`conf`	Forced information and information loss in information-theoretic competitive learning. Artificial Intelligence and Applications Ryotaro Kamimura
2007	J	`jnl`	Greedy network-growing algorithm with Minkowski distances. Int. J. Gen. Syst. Ryotaro Kamimura, Osamu Uchida, Seiki Hashimoto
2007	B	`conf`	Information loss to extract distinctive features in competitive learning. SMC Ryotaro Kamimura
2007	—	`conf`	Information-Theoretic Variable Selection in Neural Networks. FOCI Ryotaro Kamimura, Fumihiko Yoshida, Yamashita Toshie, Ryozo Kitajima
2007	—	`conf`	Interpreting cabinet approval ratings by neural networks. Artificial Intelligence and Applications Ryotaro Kamimura, Fumihiko Yoshida, Ryozo Kitajima
2007	—	`conf`	Partially Activated Neural Networks by Controlling Information. ICANN (1) Ryotaro Kamimura
2006	—	`conf`	Automatic Inference of Cabinet Approval Ratings by Information-Theoretic Competitive Learning. ICONIP (2) Ryotaro Kamimura, Fumihiko Yoshida
2006	—	`conf`	Collective Information-Theoretic Competitive Learning: Emergency of Improved Performance by Collectively Treated Neurons. ICONIP (1) Ryotaro Kamimura, Fumihiko Yoshida, Ryozo Kitajima
2006	B	`conf`	Controlling Excessive Information by Surface Information Criterion for Information-Theoretic Self-Organizing Maps. SMC Ryotaro Kamimura
2006	J	`jnl`	Cooperative information maximization with Gaussian activation functions for self-organizing maps. IEEE Trans. Neural Networks Ryotaro Kamimura
2006	—	`conf`	Self-organizing by Information Maximization: Realizing Self-Organizing Maps by Information-Theoretic Competitive Learning. ICONIP (1) Ryotaro Kamimura
2006	B	`conf`	Student Survey by Information-Theoretic Competitive Learning. SMC Ryotaro Kamimura
2006	B	`conf`	Supervised Information Maximization by Weighted Distance. IJCNN Ryotaro Kamimura
2005	—	`conf`	Extracting Common and Distinctive Features by Cost-Sensitive Information Maximization. Artificial Intelligence and Applications Ryotaro Kamimura
2005	J	`jnl`	Improving information-theoretic competitive learning by accentuated information maximization. Int. J. Gen. Syst. Ryotaro Kamimura
2005	B	`conf`	Information maximization and cost minimization in information-theoretic competitive learning. IJCNN Ryotaro Kamimura
2005	—	`conf`	Maximizing the Ratio of Information to Its Cost in Information Theoretic Competitive Learning. ICANN (2) Ryotaro Kamimura, Sachiko Aida-Hyugaji
2005	—	`conf`	Modular Structure Generation and Its Application to Feature Extraction. Artificial Intelligence and Applications Ryotaro Kamimura
2005	J	`jnl`	Unifying cost and information in information-theoretic competitive learning. Neural Networks Ryotaro Kamimura
2004	J	`jnl`	Cooperative information control for self-organizing maps. Neurocomputing Ryotaro Kamimura
2004	B	`conf`	Cost-Sensitive Greedy Network-Growing Algorithm with Gaussian Activation Functions. ICONIP Ryotaro Kamimura, Osamu Uchida
2004	B	`conf`	Greedy network-growing by Minkowski distance functions. IJCNN Ryotaro Kamimura, Osamu Uchida
2004	J	`jnl`	Improving feature extraction performance of greedy network-growing algorithm by inverse euclidean distance. Connect. Sci. Ryotaro Kamimura
2004	B	`conf`	Information maximization with Gaussian activation functions to generate explicit self-organizing maps. IJCNN Ryotaro Kamimura, Yukiko Maruyama
2004	J	`jnl`	Multi-Layered Greedy Network-Growing Algorithm: Extension of Greedy Network-Growing Algorithm to Multi-Layered Networks. Int. J. Neural Syst. Ryotaro Kamimura
2004	B	`conf`	One-Epoch Learning for Supervised Information-Theoretic Competitive Learning. ICONIP Ryotaro Kamimura
2004	B	`conf`	Teacher-Directed Learning with Gaussian and Sigmoid Activation Functions. ICONIP Ryotaro Kamimura
2004	B	`conf`	Teacher-directed information maximization: supervised information-theoretic competitive learning with Gaussian activation functions. IJCNN Ryotaro Kamimura
2003	C	`conf`	Competitive Learning by Information Maximization: Eliminating Dead Neurons in Competitive Learning. ICANN Ryotaro Kamimura
2003	C	`conf`	Economic Data Analysis and Cooperative Information Control. Modelling and Simulation Ryotaro Kamimura, Seiki Hashimoto
2003	B	`conf`	Faithful feature extraction by greedy network-growing algorithm. IJCNN Ryotaro Kamimura, Osamu Uchida
2003	B	`conf`	Flexible self-organizing maps by information maximization. IJCNN Ryotaro Kamimura, Haruhiko Takeuchi
2003	C	`conf`	Generating Explicit Self-Organizing Maps by Information Maximization. IDEAL Ryotaro Kamimura, Haruhiko Takeuchi
2003	C	`conf`	Improving Feature Extraction Performance of Greedy Network-Growing Algorithm. IDEAL Ryotaro Kamimura, Osamu Uchida
2003	B	`conf`	Improving generalization by teacher-directed learning. IJCNN Ryotaro Kamimura
2003	J	`jnl`	Information theoretic competitive learning in self-adaptive multi-layered networks. Connect. Sci. Ryotaro Kamimura
2003	J	`jnl`	Information-Theoretic Competitive Learning with Inverse Euclidean Distance Output Units. Neural Process. Lett. Ryotaro Kamimura
2003	C	`conf`	Information-theoretic Competitive Learning. Modelling and Simulation Ryotaro Kamimura
2003	J	`jnl`	Progressive Feature Extraction with a Greedy Network-growing Algorithm. Complex Syst. Ryotaro Kamimura
2003	J	`jnl`	Teacher-directed learning: information-theoretic competitive learning in supervised multi-layered networks. Connect. Sci. Ryotaro Kamimura, Fumihiko Yoshida
2002	J	`jnl`	Controlling internal representations by structural information. Neurocomputing Ryotaro Kamimura
2002	J	`jnl`	Greedy information acquisition algorithm: a new information theoretic approach to dynamic information acquisition in neural networks. Connect. Sci. Ryotaro Kamimura, Taeko Kamimura, Haruhiko Takeuchi
2001	C	`conf`	Cooperative Information Control to Coordinate Competition and Cooperation. ICANN Ryotaro Kamimura, Taeko Kamimura
2001	J	`jnl`	Flexible feature discovery and structural information control. Connect. Sci. Ryotaro Kamimura, Taeko Kamimura, Osamu Uchida
2001	C	`conf`	Information Maximization and Language Acquisition. ICANN Ryotaro Kamimura, Taeko Kamimura
2000	—	`conf`	Conditional Information Analysis. IJCNN (1) Ryotaro Kamimura
2000	B	`conf`	Information theoretic rule discovery in neural networks. SMC Ryotaro Kamimura, Taeko Kamimura
2000	—	`conf`	Selective Information Acquisition with Application to Pattern Classification. IJCNN (1) Ryotaro Kamimura
1999	B	`conf`	Controlling simple structural information to improve generalization performance. IJCNN Ryotaro Kamimura
1999	B	`conf`	Mediated and multi-level information processing. IJCNN Ryotaro Kamimura
1998	B	`conf`	Integrated Information Processors with Multi-functional Components. ICONIP Ryotaro Kamimura
1998	J	`jnl`	Minimizing alpha-Information for Generalization and Interpretation. Algorithmica Ryotaro Kamimura
1998	B	`conf`	Structural Information Control to Improve Generalization. ICONIP Ryotaro Kamimura
1997	J	`jnl`	Constrained Information Maximization to Control Internal Representatio. J. Braz. Comput. Soc. Ryotaro Kamimura
1997	J	`jnl`	Controlling α-entropy with a Neural α-Feature Detector. Complex Syst. Ryotaro Kamimura
1997	—	`conf`	D-entropy controller for interpretation and generalization. ICNN Ryotaro Kamimura
1997	—	`conf`	D-entropy minimization: integration of mutual information maximization and minimization. ICNN Ryotaro Kamimura
1997	J	`jnl`	Information Controller to Maximize and Minimize Information. Neural Comput. Ryotaro Kamimura
1996	—	`conf`	Constrained information maximization. ICNN Ryotaro Kamimura, Shohachiro Nakanishi
1996	—	`conf`	Kernel feature detector: extracting kernel features by minimizing α-information. ICNN Ryotaro Kamimura, Shohachiro Nakanishi
1996	—	`conf`	Unification of Information Maximization and Minimization. NIPS Ryotaro Kamimura
1995	—	`conf`	Discovery of Linguistic Rules by Hidden Information Maximization. ICNN Ryotaro Kamimura, Taeko Kamimura, Shohachiro Nakanishi
1995	J	`jnl`	Feature detectors by autoencoders: Decomposition of input patterns into atomic features by neural networks. Neural Process. Lett. Ryotaro Kamimura, Shohachiro Nakanishi
1995	J	`jnl`	Improving Generalization Performance by Information Minimization. IEICE Trans. Inf. Syst. Ryotaro Kamimura, Toshiyuki Takagi, Shohachiro Nakanishi
1995	—	`conf`	Information maximization for feature detection and pattern classification by autoencoders. ICNN Ryotaro Kamimura, Shohachiro Nakanishi
1995	J	`jnl`	Kernel Hidden Unit Analysis-Network Size Reduction by Entropy Minimization-. IEICE Trans. Inf. Syst. Ryotaro Kamimura, Shohachiro Nakanishi
1995	—	`conf`	Minimum α-information strategy for the interpretation of the network behaviors and the improved generalization. ICNN Ryotaro Kamimura, Shohachiro Nakanishi
1993	—	`conf`	Generation of Internal Representation by alpha. NIPS Ryotaro Kamimura
1993	—	`conf`	Minimum entropy methods in neural networks: competition and selective responses by entropy minimization. ICNN Ryotaro Kamimura
1992	B	`conf`	Competitive Learning by Entropy Minimization. ALT Ryotaro Kamimura
1992	—	`conf`	Complexity Term to Generate Explicit Internal Representation in Recurrent Neural Networks. IFIP Congress (1) Ryotaro Kamimura
1991	—	`conf`	Application of the Recurrent Neural Network to the Problem of Language Acquisition. Conference on Analysis of Neural Network Applications Ryotaro Kamimura
1990	B	`conf`	Application of temporal supervised learning algorithm to generation of natural language. IJCNN Ryotaro Kamimura
1990	—	`conf`	Recognition and restoration of periodic patterns with recurrent neural network. SPDP Ryotaro Kamimura