Shigeo Abe

Shigeo Abe, Takashi Iwai, Motohide Yoshimura, 2005, International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC'06).

Optimal Input Selection of Neural Networks by Sensitivity Analysis and Its Application to Image Recognition

Shigeo Abe, Masahiro Kayama, Masao Takatoo, 1990, MVA.

Support Vector Machines

Shigeo Abe, 2001 .

Active set training of support vector regressors

Shigeo Abe, 2010, ESANN.

Comparison of Classifier Performance

Shigeo Abe, S. Abe, 2001 .

Analysis of Multiclass Support Vector Machines

Shigeo Abe, 2002 .

Implementing Multi-class Classifiers by One-class Classification Methods

Shigeo Abe, Tao Ban, S. Abe, 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.

Preliminary Performance Evaluation of Data Flow Computers

Kotaro Hirasawa, Shigeo Abe, Yukio Kawamoto, 1982, IEEE Computer Society International Conference.

An Efficient Incremental KernelPrincipal ComponentAnalysis forOnlineFeature Selection

Shigeo Abe, Seiichi Ozawa, 2007 .

A fast incremental learning algorithm of RBF networks with long-term memory

Shigeo Abe, Seiichi Ozawa, K. Okamato, 2003, Proceedings of the International Joint Conference on Neural Networks, 2003..

Incremental Learning Algorithm for Feedforward Neural Network with Long-Term Memory

Shigeo Abe, Seiichi Ozawa, Masataka Kobayashi, 2002 .

Image Query by Multiresolution Spectral Histograms

Shigeo Abe, Motohide Yoshimura, Noriaki Kawamura, 2005, International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC'06).

Tuning of a fuzzy classifier derived from data

Shigeo Abe, Ming-Shong Lan, Ruck Thawonmas, 1994, Proceedings of 1994 IEEE 3rd International Fuzzy Systems Conference.

Modified backward feature selection by cross validation

Shigeo Abe, S. Abe, 2005, ESANN.

Function Approximation with Partitioned Ellipsoidal Regions

Shigeo Abe, Ruck Thawonmas, S. Abe, 1997, ICONIP.

Optimizing kernel parameters by second-order methods

Shigeo Abe, 2007, ESANN.

Support Vector Machines for Pattern Classification

Shigeo Abe, 2010, Advances in Pattern Recognition.

Maximum Margin Fuzzy Classifiers with Ellipsoidal Regions

Shigeo Abe, S. Abe, 2002 .

Sparse support vector machines trained in the reduced empirical feature space

Shigeo Abe, Kazuki Iwamura, S. Abe, 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).

Incremental learning of feature space and classifier for face recognition

Shigeo Abe, Shaoning Pang, Nikola K. Kasabov, 2005, Neural Networks.

The Hopfield Network

Shigeo Abe, 1997 .

Subspace based linear programming support vector machines

Shigeo Abe, Takuya Kitamura, Kazuhiro Fukui, 2009, 2009 International Joint Conference on Neural Networks.

Maximizing margins of multilayer neural networks

Shigeo Abe, T. Nishikawa, S. Abe, 2002, Proceedings of the 9th International Conference on Neural Information Processing, 2002. ICONIP '02..

Subspace based least squares support vector machines for pattern classification

Shigeo Abe, Takuya Kitamura, Kazuhiro Fukui, 2009, 2009 International Joint Conference on Neural Networks.

Extraction of Fuzzy Rules for Classification Based on Partitioned Hyperboxes

Shigeo Abe, Ruck Thawonmas, S. Abe, 1996, J. Intell. Fuzzy Syst..

Fuzzy Rule Generation

Shigeo Abe, 2001 .

Support Vector Machines for Pattern Classification (Advances in Pattern Recognition)

Shigeo Abe, 2005 .

Determination of Power System Voltage Stability Part 3: Dynamical Approach

Shigeo Abe, Akira Isono, Bunji Kondo, 2007 .

Solving inequality constrained combinatorial optimization problems by the hopfield neural networks

Kotaro Hirasawa, Shigeo Abe, Junzo Kawakami, 1992, Neural Networks.

Feature Extraction Using Support Vector Machines

Shigeo Abe, Takuya Kitamura, Yasuyuki Tajiri, 2010, ICONIP.

Decomposition techniques for training linear programming support vector machines

Shigeo Abe, Yusuke Torii, S. Abe, 2009, Neurocomputing.

Variants of Support Vector Machines

Shigeo Abe, S. Abe, 2010 .

Remote control video camera for meteor observations

Shigeo Abe, Pavel Koten, P. Spurny, 2006 .

Input Layer Optimization of Neural Networks by Sensitivity Analysis and its Application to Recognition of Numerals

Shigeo Abe, Masahiro Kayama, Tadaaki Kitamura, 1991 .

Generation of Training and Test Data Sets

Shigeo Abe, 2001 .

Feature reduction based on analysis of fuzzy regions

Shigeo Abe, Ruck Thawonmas, 1995, Proceedings of ICNN'95 - International Conference on Neural Networks.

A genetic algorithm approach to multi-objective scheduling problems with earliness and tardiness penalties

Shigeo Abe, Hisashi Tamaki, Etsuo Nishino, 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

Feature selection based on kernel discriminant analysis for multi-class problems

Shigeo Abe, Tsuneyoshi Ishii, 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).

Backward Varilable Selection of Support Vector Regressors by Block Deletion

Shigeo Abe, Takashi Nagatani, 2007, 2007 International Joint Conference on Neural Networks.

Support Vector Machines for Pattern Classification-V : Feature Extraction and Feature Selection

Shigeo Abe, 2009 .

Subspace-Based L2 Support Vector Machines

Shigeo Abe, Takuya Kitamura, 2010, Aust. J. Intell. Inf. Process. Syst..

Fuzzy Rule Extraction for Pattern Classification from Numerical Data

Shigeo Abe, 1997 .

A Memory-Based Reinforcement Learning Algorithm to Prevent Unlearning in Neural Networks

Shigeo Abe, Seiichi Ozawa, 2004 .

Online Incremental Face Recognition System Using Eigenface Feature and Neural Classifier

Shigeo Abe, Nikola Kasabov, Shaoning Pang, 2009 .

Training three-layer neural network classifiers by solving inequalities

Naoki Tsuchiya, Shigeo Abe, Seiichi Ozawa, 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.

Feature selection by analyzing class regions approximated by ellipsoids

Yoshiki Kobayashi, Shigeo Abe, Ruck Thawonmas, 1998, IEEE Trans. Syst. Man Cybern. Part C.

Incremental Feature Selection by Block Addition and Block Deletion Using Least Squares SVRs

Shigeo Abe, 2014, ANNPR.

Fuzzy LP-SVMs for Multiclass Problems

Shigeo Abe, 2004, ESANN.

Performance Improvement fo Dynamical Associative Memories by Solving Linear Inequalities

Shigeo Abe, Seiichi Ozawa, Jungpil Park, 2001 .

Other Neural Networks

Shigeo Abe, 1997 .

A fuzzy classifier based on partitioned hyperboxes

Shigeo Abe, Ruck Thawonmas, S. Abe, 1996, Proceedings of International Conference on Neural Networks (ICNN'96).

Feature selection and fast training of subspace based support vector machines

Shigeo Abe, Takuya Kitamura, Syogo Takeuchi, 2010, The 2010 International Joint Conference on Neural Networks (IJCNN).

Subspace-based support vector machines for pattern classification

Shigeo Abe, Takuya Kitamura, Kazuhiro Fukui, 2009, Neural Networks.

Fast Variable Selection by Block Addition and Block Deletion

Shigeo Abe, Takashi Nagatani, Seiichi Ozawa, 2010, J. Intell. Learn. Syst. Appl..

Extracting algorithms from pattern classification neural networks

Shigeo Abe, Masahiro Kayama, Tadaaki Kitamura, 1993, Neural Networks.

Multilayer Neural Network Classifiers

Shigeo Abe, 2001 .

Fuzzy Systems with Learning Capability

Shigeo Abe, 1995, Fuzzy Logic in Artificial Intelligence.

Overview of Neural Networks

Shigeo Abe, 1997 .

Training of a fuzzy classifier with ellipsoidal regions by dynamic cluster generation

Shigeo Abe, 1998, 1998 Second International Conference. Knowledge-Based Intelligent Electronic Systems. Proceedings KES'98 (Cat. No.98EX111).

Fuzzy Rule Extraction with Optimized Hyperboxes for Approximating Class Regions

Shigeo Abe, Ruck Thawonmas, 1995 .

Batch Support Vector Training Based on Exact Incremental Training

Shigeo Abe, 2008, ICANN.

Tuning membership functions of kernel fuzzy classifiers by maximizing margins

Shigeo Abe, Seiichi Ozawa, Kazuya Morikawa, 2009, Memetic Comput..

Tuning of Membership Functions

Shigeo Abe, 2001 .

Multiple Nonlinear Subspace Methods Using Subspace-based Support Vector Machines

Shigeo Abe, Takuya Kitamura, Yusuke Tanaka, 2011, 2011 10th International Conference on Machine Learning and Applications and Workshops.

Feature Selection by Block Addition and Block Deletion

Shigeo Abe, Takashi Nagatani, 2012, ANNPR.

Sparse kernel feature analysis using FastMap and its variants

Shigeo Abe, Youki Kadobayashi, Tao Ban, 2009, 2009 International Joint Conference on Neural Networks.

Analyzing Minimal Complexity Machines

Shigeo Abe, 2019, 2019 International Joint Conference on Neural Networks (IJCNN).

Comparison of sparse least squares support vector regressors trained in primal and dual

Shigeo Abe, 2008, ESANN.

A new approach to discover interlacing data structures in high-dimensional space

Shigeo Abe, Changshui Zhang, Tao Ban, 2009, Journal of Intelligent Information Systems.

Numerical Study on a Continuous Age Model of Genetic Algorithms for Uncertain Optimization Problems

Shigeo Abe, Hisashi Tamaki, Shinzo Kitamura, 2000 .

An Incremental Learning Algorithm of Ensemble Classifier Systems

Shigeo Abe, Seiichi Ozawa, Takuya Kidera, 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.

Fuzzy Classifiers Based on Kernel Discriminant Analysis

Shigeo Abe, Ryota Hosokawa, 2007, ICANN.

Neural networks as a tool to generate pattern classification algorithms

Shigeo Abe, Masahiro Kayama, Tadaaki Kitamura, 1992, [Proceedings 1992] IJCNN International Joint Conference on Neural Networks.

Training neural net classifier to improve generalization capability

Shigeo Abe, Masahiro Kayama, S. Abe, 1994, Systems and Computers in Japan.

A method for fuzzy rules extraction directly from numerical data and its application to pattern classification

Shigeo Abe, Ming-Shong Lan, S. Abe, 1995, IEEE Trans. Fuzzy Syst..

Training of Support Vector Machines with Mahalanobis Kernels

Shigeo Abe, 2005, ICANN.

Advanced Image Retrieval Using Multi-resolution Image Content

Shigeo Abe, Motohide Yoshimura, Hajime Kiyose, 2002, MVA.

Multiclass Support Vector Machines

Shigeo Abe, S. Abe, 2010 .

Sparse support vector machines by kernel discriminant analysis

Shigeo Abe, Kazuki Iwamura, 2009, ESANN.

Function approximation based on fuzzy rules extracted from partitioned numerical data

Shigeo Abe, Ruck Thawonmas, 1999, IEEE Trans. Syst. Man Cybern. Part B.

Comments on: Support vector machines maximizing geometric margins for multi-class classification

Shigeo Abe, 2014 .

Sparse support vector regressors based on forward basis selection

Shigeo Abe, Shigenori Muraoka, 2009, 2009 International Joint Conference on Neural Networks.

Studies on the American Style Purse Seiner-II Comparision with the Stern Trawler

Shigeo Abe, 1977 .

Incremental Kernel PCA for Online Learning of Feature Space

Shigeo Abe, Seiichi Ozawa, Shosuke Kimura, 2005, International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC'06).

Fuzzy rules extraction directly from numerical data for function approximation

Shigeo Abe, Ming-Shong Lan, S. Abe, 1995, IEEE Trans. Syst. Man Cybern..

Selecting Support Vector Candidates for Incremental Training

Shigeo Abe, Shinya Katagiri, 2005, 2005 IEEE International Conference on Systems, Man and Cybernetics.

A novel approach to feature selection based on analysis of class regions

Shigeo Abe, Ruck Thawonmas, S. Abe, 1997, IEEE Trans. Syst. Man Cybern. Part B.

Feature Selection by Iterative Block Addition and Block Deletion

Shigeo Abe, S. Abe, 2013, 2013 IEEE International Conference on Systems, Man, and Cybernetics.

A continuous age model of genetic algorithms applicable to optimization problems with uncertainties

Shigeo Abe, Hisashi Tamaki, Shinzo Kitamura, 1999, IEEE SMC'99 Conference Proceedings. 1999 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.99CH37028).

Robust Pattern Classification

Shigeo Abe, 2001 .

Fast feature selection by analyzing class regions approximated by ellipsoids

Shigeo Abe, Kota Kawaratani, S. Abe, 1998, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.

Optimizing working sets for training support vector regressors by Newton's method

Shigeo Abe, S. Abe, 2015, 2015 International Joint Conference on Neural Networks (IJCNN).

Feature Selection and Extraction

Shigeo Abe, 2010 .

Modeling and Genetic Solution for Scheduling Problems with Regular and Non-Regular Objective Functions

Shigeo Abe, Hisashi Tamaki, Etsuo Nishino, 1999 .

Improving Generalization Abilities of Maximal Average Margin Classifiers

Shigeo Abe, S. Abe, 2016, ANNPR.

Tuning of a fuzzy classifier derived from data

Shigeo Abe, Ming-Shong Lan, Ruck Thawonmas, 1996, Int. J. Approx. Reason..

Input Layer Optimization of Neural Networks by Sensitivity Analysis and its Application to Recognition of Numerals

Shigeo Abe, Masahiro Kayama, Masao Takatoo, 1991 .

Maximum-Margin Multilayer Neural Networks

Shigeo Abe, 2003 .

Evaluation of Feature Selection by Multiclass Kernel Discriminant Analysis

Shigeo Abe, Tsuneyoshi Ishii, S. Abe, 2010, ANNPR.

Training Mahalanobis Kernels by Linear Programming

Shigeo Abe, 2012, ICANN.

A fuzzy classifier with ellipsoidal regions for diagnosis problems

Shigeo Abe, Ruck Thawonmas, Masahiro Kayama, 1999, IEEE Trans. Syst. Man Cybern. Part C.

A Learning Algorithm of Boosting Kernel Discriminant Analysis for Pattern Recognition

Shigeo Abe, Seiichi Ozawa, Satoshi Maekawa, 2007, IEICE Trans. Inf. Syst..

Robust function approximation using fuzzy rules with ellipsoidal regions

Shigeo Abe, Hisashi Tamaki, Hiroyasu Kubota, 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.

Neural Networks and Fuzzy Systems: Theory and Applications

Shigeo Abe, Anca Ralescu, 2012 .

Incremental Input Variable Selection by Block Addition and Block Deletion

Shigeo Abe, 2014, ICANN.

Rule acquisition based on hyperbox representation and its applications

Shigeo Abe, Ruck Thawonmas, 1998, 1998 Second International Conference. Knowledge-Based Intelligent Electronic Systems. Proceedings KES'98 (Cat. No.98EX111).

A Fast Incremental Kernel Principal Component Analysis for Online Feature Extraction

Shigeo Abe, Seiichi Ozawa, Yohei Takeuchi, 2010, PRICAI.

KPCA-based training of a kernel fuzzy classifier with ellipsoidal regions

Shigeo Abe, Kenichi Kaieda, S. Abe, 2004, Int. J. Approx. Reason..

Pattern Classification

Shigeo Abe, S. Abe, 2001 .

Fuzzy Clustering Networks : Design Criteria for Approximation and Prediction

Shigeo Abe, John Mitchell, 1996 .

Incremental training of support vector machines using hyperspheres

Shigeo Abe, Shinya Katagiri, S. Abe, 2006, Pattern Recognit. Lett..

Effect of Equality Constraints to Unconstrained Large Margin Distribution Machines

Shigeo Abe, S. Abe, 2018, ANNPR.

A New Optimization Technique for a Prolog Computer

Shigeo Abe, Ken-ichi Kurosawa, Kaori Kiriyama, 1986, COMPCON.

Fuzzy support vector machines for multiclass problems

Shigeo Abe, Takuya Inoue, 2002, ESANN.

How neural networks for pattern recognition can be synthesized

Shigeo Abe, Masahiro Kayama, Hiroshi Takenaga, 1991 .

Sparse Least Squares Support Vector Regressors Trained in the Reduced Empirical Feature Space

Shigeo Abe, Kenta Onishi, S. Abe, 2007, ICANN.

Sparse least squares support vector training in the reduced empirical feature space

Shigeo Abe, S. Abe, 2007, Pattern Analysis and Applications.

Efficient methods for fuzzy rule extraction from numerical data

Shigeo Abe, Ming-Shong Lan, 1996 .

An Efficient Incremental Kernel Principal Component Analysis for Online Feature Selection

Shigeo Abe, Seiichi Ozawa, Yohei Takeuchi, 2007, 2007 International Joint Conference on Neural Networks.

Fast Training of Support Vector Machines by Extracting Boundary Data

Shigeo Abe, Takuya Inoue, 2001, ICANN.

Reducing computations in incremental learning for feedforward neural network with long-term memory

Shigeo Abe, Seiichi Ozawa, M. Kobyashi, 2001, IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222).

Fuzzy Rule Extraction for Function Approximation from Numerical Data

Shigeo Abe, 1997 .

High Speed Training of a Fuzzy Classifier with Ellipsoidal Regions

Shigeo Abe, Takushi Eguchi, S. Abe, 1999 .

Are twin hyperplanes necessary?

Shigeo Abe, 2018, Pattern Recognit. Lett..

Unconstrained large margin distribution machines

Shigeo Abe, S. Abe, 2017, Pattern Recognit. Lett..

Boosting Kernel Discriminant Analysis with Adaptive Kernel Selection

Shigeo Abe, Seiichi Ozawa, Satoshi Maekawa, 2005 .

Kobe University Repository : Kernel Title Training

Shigeo Abe, Mahalanobis Kernels, 2015 .

Performance evaluation of Integrated Prolog Processor IPP

Shigeo Abe, Tadaaki Bandoh, K. Kiriyama, 1988, Proceedings of the International Workshop on Artificial Intelligence for Industrial Applications.

Is Primal Better Than Dual

Shigeo Abe, 2009, ICANN.

Dynamic Fuzzy Rule Generation

Shigeo Abe, 2001 .

Fast training of a fuzzy classifier with ellipsoidal regions

Shigeo Abe, Ruck Thawonmas, 1996, Proceedings of IEEE 5th International Fuzzy Systems.

Dependency of Generalization Capability for a Multi-Layered Neural Network on its Number of Hidden Units

Shigeo Abe, Yasuo Morooka, Masahiro Kayama, 1993 .

Convergence improvement of active set support vector training

Shigeo Abe, Ryousuke Yabuwaki, 2010, The 2010 International Joint Conference on Neural Networks (IJCNN).

Static Fuzzy Rule Generation

Shigeo Abe, 2001 .

LSI module placement by the Hopfield neural network

Shigeo Abe, 1995 .

Synthesizing neural networks for pattern recognition

Shigeo Abe, H. Takenaga, M. Kayama, 1991, [Proceedings] 1991 IEEE International Joint Conference on Neural Networks.

Evaluation Of Memory System For Integrated Prolog Processor IPP

Shigeo Abe, Tadaaki Bandoh, S. Yamaguchi, 1989, The 16th Annual International Symposium on Computer Architecture.

An Application of Genetics-Based Machine Learning Approach to a Realtime Assignment Problem

Shigeo Abe, Hisashi Tamaki, Shinzo Kitamura, 2001 .

Sparse Least Squares Support Vector Machines by Forward Selection Based on Linear Discriminant Analysis

Shigeo Abe, 2008, ANNPR.

Kernel discriminant analysis based feature selection

Shigeo Abe, Tsuneyoshi Ishii, Masamichi Ashihara, 2008, Neurocomputing.

Generalization improvement of a fuzzy classifier with pyramidal membership functions

Shigeo Abe, 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

A fuzzy classifier with ellipsoidal regions

Shigeo Abe, Ruck Thawonmas, S. Abe, 1997, IEEE Trans. Fuzzy Syst..

Generalization improvement of a fuzzy classifier with ellipsoidal regions

Shigeo Abe, Keita Sakaguchi, S. Abe, 2001, 10th IEEE International Conference on Fuzzy Systems. (Cat. No.01CH37297).

Overview of Fuzzy Systems

Shigeo Abe, 1997 .

Support Vector Regression Using Mahalanobis Kernels

Shigeo Abe, Yuya Kamada, S. Abe, 2006, ANNPR.

Feature Selection Based on Kernel Discriminant Analysis

Shigeo Abe, Masamichi Ashihara, 2006, ICANN.

Convergence Improvement of Active Set Training for Support Vector Regressors

Shigeo Abe, Ryousuke Yabuwaki, 2010, ICANN.

Fast Training of Linear Programming Support Vector Machines Using Decomposition Techniques

Shigeo Abe, Yusuke Torii, S. Abe, 2006, ANNPR.

Fast Training of Support Vector Machines and Performance Comparison with Fuzzy Classifiers

Shigeo Abe, Hisashi Tamaki, Takuya Inoue, 2002 .

Image retrieval by multi-resolution image content

Shigeo Abe, Motohide Yoshimura, Hajime Kiyose, 2002 .

Fuzzy least squares support vector machines for multiclass problems

Shigeo Abe, Daisuke Tsujinishi, S. Abe, 2003, Neural Networks.

A Fuzzy Classifier with Pyramidal Membership Functions

Shigeo Abe, 2003 .

Fast Training of Three-Layered Neural Network Classifiers by Solving Inequalities

Naoki Tsuchiya, Shigeo Abe, Seiichi Ozawa, 2000 .

Support Vector Machines for Pattern Classification

Shigeo Abe, S. Abe, 1999, Advances in Pattern Recognition.

Neural Networks and Fuzzy Systems

Shigeo Abe, S. Abe, 1996, Springer US.

Determination of power system voltage stability. Part 2: Digital simulation

Shigeo Abe, Akira Isono, 1976 .

Two-Class Support Vector Machines

Shigeo Abe, S. Abe, 2010 .

Pattern Classification: Neuro-fuzzy Methods and Their Comparison

Shigeo Abe, S. Abe, 2012 .

Detection of Cell Forms in Multicellular Objects

Shigeo Abe, Kohei Asano, Motohide Yoshimura, 2005, International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC'06).

Fusing sequential minimal optimization and Newton’s method for support vector training

Shigeo Abe, S. Abe, 2014, International Journal of Machine Learning and Cybernetics.

Mining interlacing manifolds in high dimensional spaces

Shigeo Abe, Changshui Zhang, Youki Kadobayashi, 2011, SAC '11.

Incremental Training of Support Vector Machines Using Truncated Hypercones

Shigeo Abe, Shinya Katagiri, 2006, ANNPR.

Soft Upper-bound Minimal Complexity LP SVMs

Shigeo Abe, 2021, 2021 International Joint Conference on Neural Networks (IJCNN).

Global convergence and suppression of spurious states of the Hopfield neural networks

Shigeo Abe, 1991, [Proceedings] 1991 IEEE International Joint Conference on Neural Networks.

Tuning of a fuzzy classifier by solving inequalities

Shigeo Abe, Ming-Shong Lan, Ruck Thawonmas, 1995, Proceedings of 1995 IEEE International Conference on Fuzzy Systems..

A Genetic Algorithm Approach to Optimization Problems with Uncertainties

Shigeo Abe, Hisashi Tamaki, Toshihiko Arai, 1999 .

Tuning membership locations of a fuzzy classifier with ellipsoidal regions

Shigeo Abe, S. Abe, K. Sakaguchi, 2001, IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222).

A function approximator using fuzzy rules extracted directly from numerical data

Shigeo Abe, Ming-Shong Lan, 1993, Proceedings of 1993 International Conference on Neural Networks (IJCNN-93-Nagoya, Japan).

Comparison of L1 and L2 support vector machines

Shigeo Abe, S. Abe, Y. Koshiba, 2003, Proceedings of the International Joint Conference on Neural Networks, 2003..

Determination of Power System Voltage Stability Part 3: Dynamical Approach

Shigeo Abe, Akira Isono, Bunji Kondo, 1983 .

Improvement of Generalization Ability of Multiclass Support Vector Machines by Introducing Fuzzy Logic and Bayes Theory

Shigeo Abe, Takuya Inoue, 2002 .

A kernel fuzzy classifier with ellipsoidal regions

Shigeo Abe, S. Abe, K. Kaieda, 2003, Proceedings of the International Joint Conference on Neural Networks, 2003..

DETERMINING WEIGHTS OF THE HOPFIELD NEURAL NETWORKS

Shigeo Abe, 1991 .

Decision-Tree-Based Support Vector Machines

Shigeo Abe, Fumitake Takahashi, 2003 .

Fuzzy least squares support vector machines

Shigeo Abe, S. Abe, D. Tsujinishi, 2003, Proceedings of the International Joint Conference on Neural Networks, 2003..

Determination of Power System Voltage Stability, Part 1: Theory

Shigeo Abe, Akira Isono, S. Abe, 1976 .

Character recognition using fuzzy rules extracted from data

Shigeo Abe, Ming-Shong Lan, Hiroshi Takenaga, 1994, Proceedings of 1994 IEEE 3rd International Fuzzy Systems Conference.