Shigeo Abe

According to our database, Shigeo Abe authored at least 118 papers between 1982 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Are Bounded Support Vectors Harmful to Stable Classification.

Shigeo Abe

Proceedings of the International Joint Conference on Neural Networks, 2024

Evaluating Support Vector Machines with Multiple Kernels by Random Search.

Shigeo Abe

Proceedings of the Artificial Neural Networks in Pattern Recognition, 2024

2023

Training Minimal Complexity Support Vector Machines with Multiple Kernels.

Shigeo Abe

Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, 2023

2022

Soft Upper-bound Support Vector Machines.

Shigeo Abe

Proceedings of the International Joint Conference on Neural Networks, 2022

Do Minimal Complexity Least Squares Support Vector Machines Work?

Shigeo Abe

Proceedings of the Artificial Neural Networks in Pattern Recognition, 2022

2021

Soft Upper-bound Minimal Complexity LP SVMs.

Shigeo Abe

Proceedings of the International Joint Conference on Neural Networks, 2021

2020

Minimal Complexity Support Vector Machines for Pattern Classification.

Shigeo Abe

Comput., 2020

Minimal Complexity Support Vector Machines.

Shigeo Abe

Proceedings of the Artificial Neural Networks in Pattern Recognition, 2020

2019

Analyzing Minimal Complexity Machines.

Shigeo Abe

Proceedings of the International Joint Conference on Neural Networks, 2019

2018

Are twin hyperplanes necessary?

Shigeo Abe

Pattern Recognit. Lett., 2018

Effect of Equality Constraints to Unconstrained Large Margin Distribution Machines.

Shigeo Abe

Proceedings of the Artificial Neural Networks in Pattern Recognition, 2018

2017

Unconstrained large margin distribution machines.

Shigeo Abe

Pattern Recognit. Lett., 2017

2016

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

Shigeo Abe

Int. J. Mach. Learn. Cybern., 2016

Improving Generalization Abilities of Maximal Average Margin Classifiers.

Shigeo Abe

Proceedings of the Artificial Neural Networks in Pattern Recognition, 2016

2015

Fuzzy support vector machines for multilabel classification.

Shigeo Abe

Pattern Recognit., 2015

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

Shigeo Abe

Proceedings of the 2015 International Joint Conference on Neural Networks, 2015

2014

Incremental Input Variable Selection by Block Addition and Block Deletion.

Shigeo Abe

Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2014, 2014

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

Shigeo Abe

Proceedings of the Artificial Neural Networks in Pattern Recognition, 2014

2013

Feature Selection by Iterative Block Addition and Block Deletion.

Shigeo Abe

Proceedings of the IEEE International Conference on Systems, 2013

2012

Training Mahalanobis Kernels by Linear Programming.

Shigeo Abe

Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2012, 2012

Feature Selection by Block Addition and Block Deletion.

Takashi Nagatani, Shigeo Abe

Proceedings of the Artificial Neural Networks in Pattern Recognition, 2012

2011

Mining interlacing manifolds in high dimensional spaces.

Tao Ban, Changshui Zhang, Shigeo Abe, Takeshi Takahashi, Youki Kadobayashi

Proceedings of the 2011 ACM Symposium on Applied Computing (SAC), TaiChung, Taiwan, March 21, 2011

Multiple Nonlinear Subspace Methods Using Subspace-based Support Vector Machines.

Takuya Kitamura, Shigeo Abe, Yusuke Tanaka

Proceedings of the 10th International Conference on Machine Learning and Applications and Workshops, 2011

Fast Support Vector Training by Newton's Method.

Shigeo Abe

Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2011, 2011

2010

Support Vector Machines for Pattern Classification.

Shigeo Abe

Advances in Pattern Recognition, Springer, ISBN: 978-1-84996-098-4, 2010

Fast Variable Selection by Block Addition and Block Deletion.

Takashi Nagatani, Seiichi Ozawa, Shigeo Abe

J. Intell. Learn. Syst. Appl., 2010

Subspace-Based L2 Support Vector Machines.

Takuya Kitamura, Shigeo Abe

Aust. J. Intell. Inf. Process. Syst., 2010

A Fast Incremental Kernel Principal Component Analysis for Online Feature Extraction.

Seiichi Ozawa, Yohei Takeuchi, Shigeo Abe

Proceedings of the PRICAI 2010: Trends in Artificial Intelligence, 2010

Convergence improvement of active set support vector training.

Ryousuke Yabuwaki, Shigeo Abe

Proceedings of the International Joint Conference on Neural Networks, 2010

Feature selection and fast training of subspace based support vector machines.

Takuya Kitamura, Syogo Takeuchi, Shigeo Abe

Proceedings of the International Joint Conference on Neural Networks, 2010

Feature Extraction Using Support Vector Machines.

Yasuyuki Tajiri, Ryousuke Yabuwaki, Takuya Kitamura, Shigeo Abe

Proceedings of the Neural Information Processing. Models and Applications, 2010

Convergence Improvement of Active Set Training for Support Vector Regressors.

Shigeo Abe, Ryousuke Yabuwaki

Proceedings of the Artificial Neural Networks, 2010

Active set training of support vector regressors.

Shigeo Abe

Proceedings of the 18th European Symposium on Artificial Neural Networks, 2010

Evaluation of Feature Selection by Multiclass Kernel Discriminant Analysis.

Tsuneyoshi Ishii, Shigeo Abe

Proceedings of the Artificial Neural Networks in Pattern Recognition, 2010

2009

Subspace-based support vector machines for pattern classification.

Takuya Kitamura, Syogo Takeuchi, Shigeo Abe, Kazuhiro Fukui

Neural Networks, 2009

Tuning membership functions of kernel fuzzy classifiers by maximizing margins.

Kazuya Morikawa, Seiichi Ozawa, Shigeo Abe

Memetic Comput., 2009

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

Tao Ban, Changshui Zhang, Shigeo Abe

J. Intell. Inf. Syst., 2009

Decomposition techniques for training linear programming support vector machines.

Yusuke Torii, Shigeo Abe

Neurocomputing, 2009

Subspace based linear programming support vector machines.

Syogo Takeuchi, Takuya Kitamura, Shigeo Abe, Kazuhiro Fukui

Proceedings of the International Joint Conference on Neural Networks, 2009

Sparse support vector regressors based on forward basis selection.

Shigenori Muraoka, Shigeo Abe

Proceedings of the International Joint Conference on Neural Networks, 2009

Subspace based least squares support vector machines for pattern classification.

Takuya Kitamura, Shigeo Abe, Kazuhiro Fukui

Proceedings of the International Joint Conference on Neural Networks, 2009

Sparse kernel feature analysis using FastMap and its variants.

Tao Ban, Youki Kadobayashi, Shigeo Abe

Proceedings of the International Joint Conference on Neural Networks, 2009

Is Primal Better Than Dual.

Shigeo Abe

Proceedings of the Artificial Neural Networks, 2009

Sparse support vector machines by kernel discriminant analysis.

Kazuki Iwamura, Shigeo Abe

Proceedings of the 17th European Symposium on Artificial Neural Networks, 2009

2008

Kernel discriminant analysis based feature selection.

Tsuneyoshi Ishii, Masamichi Ashihara, Shigeo Abe

Neurocomputing, 2008

Sparse support vector machines trained in the reduced empirical feature space.

Kazuki Iwamura, Shigeo Abe

Proceedings of the International Joint Conference on Neural Networks, 2008

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

Tsuneyoshi Ishii, Shigeo Abe

Proceedings of the International Joint Conference on Neural Networks, 2008

Improved Parameter Tuning Algorithms for Fuzzy Classifiers.

Kazuya Morikawa, Shigeo Abe

Proceedings of the Advances in Neuro-Information Processing, 15th International Conference, 2008

Batch Support Vector Training Based on Exact Incremental Training.

Shigeo Abe

Proceedings of the Artificial Neural Networks, 2008

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

Shigeo Abe

Proceedings of the 16th European Symposium on Artificial Neural Networks, 2008

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

Shigeo Abe

Proceedings of the Artificial Neural Networks in Pattern Recognition, Third IAPR Workshop, 2008

2007

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

Shigeo Abe

Pattern Anal. Appl., 2007

A Learning Algorithm of Boosting Kernel Discriminant Analysis for Pattern Recognition.

Shinji Kita, Seiichi Ozawa, Satoshi Maekawa, Shigeo Abe

IEICE Trans. Inf. Syst., 2007

An Efficient Incremental Kernel Principal Component Analysis for Online Feature Selection.

Yohei Takeuchi, Seiichi Ozawa, Shigeo Abe

Proceedings of the International Joint Conference on Neural Networks, 2007

Backward Varilable Selection of Support Vector Regressors by Block Deletion.

Takashi Nagatani, Shigeo Abe

Proceedings of the International Joint Conference on Neural Networks, 2007

Fuzzy Classifiers Based on Kernel Discriminant Analysis.

Ryota Hosokawa, Shigeo Abe

Proceedings of the Artificial Neural Networks, 2007

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

Shigeo Abe, Kenta Onishi

Proceedings of the Artificial Neural Networks, 2007

Optimizing kernel parameters by second-order methods.

Shigeo Abe

Proceedings of the 15th European Symposium on Artificial Neural Networks, 2007

2006

Incremental training of support vector machines using hyperspheres.

Shinya Katagiri, Shigeo Abe

Pattern Recognit. Lett., 2006

An Incremental Learning Algorithm of Ensemble Classifier Systems.

Takuya Kidera, Seiichi Ozawa, Shigeo Abe

Proceedings of the International Joint Conference on Neural Networks, 2006

Implementing Multi-class Classifiers by One-class Classification Methods.

Tao Ban, Shigeo Abe

Proceedings of the International Joint Conference on Neural Networks, 2006

Feature Selection Based on Kernel Discriminant Analysis.

Masamichi Ashihara, Shigeo Abe

Proceedings of the Artificial Neural Networks, 2006

Fast Training of Linear Programming Support Vector Machines Using Decomposition Techniques.

Yusuke Torii, Shigeo Abe

Proceedings of the Artificial Neural Networks in Pattern Recognition, Second IAPR Workshop, 2006

Incremental Training of Support Vector Machines Using Truncated Hypercones.

Shinya Katagiri, Shigeo Abe

Proceedings of the Artificial Neural Networks in Pattern Recognition, Second IAPR Workshop, 2006

Support Vector Regression Using Mahalanobis Kernels.

Yuya Kamada, Shigeo Abe

Proceedings of the Artificial Neural Networks in Pattern Recognition, Second IAPR Workshop, 2006

2005

Support Vector Machines for Pattern Classification.

Shigeo Abe

Advances in Pattern Recognition, Springer, ISBN: 978-1-84628-219-5, 2005

Comparison between error correcting output codes and fuzzy support vector machines.

Tomonori Kikuchi, Shigeo Abe

Pattern Recognit. Lett., 2005

Incremental learning of feature space and classifier for face recognition.

Seiichi Ozawa, Soon Lee Toh, Shigeo Abe, Shaoning Pang, Nikola K. Kasabov

Neural Networks, 2005

Selecting Support Vector Candidates for Incremental Training.

Shinya Katagiri, Shigeo Abe

Proceedings of the IEEE International Conference on Systems, 2005

Training of Support Vector Machines with Mahalanobis Kernels.

Shigeo Abe

Proceedings of the Artificial Neural Networks: Formal Models and Their Applications, 2005

Modified backward feature selection by cross validation.

Shigeo Abe

Proceedings of the 13th European Symposium on Artificial Neural Networks, 2005

Incremental Kernel PCA for Online Learning of Feature Space.

Shosuke Kimura, Seiichi Ozawa, Shigeo Abe

Proceedings of the 2005 International Conference on Computational Intelligence for Modelling Control and Automation (CIMCA 2005), 2005

Image Query by Multiresolution Spectral Histograms.

Noriaki Kawamura, Motohide Yoshimura, Shigeo Abe

Proceedings of the 2005 International Conference on Computational Intelligence for Modelling Control and Automation (CIMCA 2005), 2005

Detection of Protein Crystallizations under Dynamic Environment.

Takashi Iwai, Motohide Yoshimura, Shigeo Abe

Proceedings of the 2005 International Conference on Computational Intelligence for Modelling Control and Automation (CIMCA 2005), 2005

Detection of Cell Forms in Multicellular Objects.

Kohei Asano, Motohide Yoshimura, Shigeo Abe

Proceedings of the 2005 International Conference on Computational Intelligence for Modelling Control and Automation (CIMCA 2005), 2005

2004

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

Kenichi Kaieda, Shigeo Abe

Int. J. Approx. Reason., 2004

Fuzzy LP-SVMs for Multiclass Problems.

Shigeo Abe

Proceedings of the 12th European Symposium on Artificial Neural Networks, 2004

2003

Fuzzy least squares support vector machines for multiclass problems.

Daisuke Tsujinishi, Shigeo Abe

Neural Networks, 2003

2002

Analysis of support vector machines.

Shigeo Abe

Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing, 2002

Advanced Image Retrieval Using Multi-resolution Image Content.

Motohide Yoshimura, Hajime Kiyose, Shigeo Abe

Proceedings of the IAPR Conference on Machine Vision Applications (IAPR MVA 2002), 2002

Fuzzy support vector machines for multiclass problems.

Shigeo Abe, Takuya Inoue

Proceedings of the 10th Eurorean Symposium on Artificial Neural Networks, 2002

2001

Fast Training of Support Vector Machines by Extracting Boundary Data.

Shigeo Abe, Takuya Inoue

Proceedings of the Artificial Neural Networks, 2001

Generalization Improvement of a Fuzzy Classifier With Ellipsodial Regions.

Shigeo Abe, Keita Sakaguchi

Proceedings of the 10th IEEE International Conference on Fuzzy Systems, 2001

2000

Training Three-Layer Neural Network Classifiers by Solving Inequalities.

Naoki Tsuchiya, Seiichi Ozawa, Shigeo Abe

Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks, 2000

Robust Function Approximation Using Fuzzy Rules with Ellipsoidal Regions.

Hiroyasu Kubota, Hisashi Tamaki, Shigeo Abe

Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks, 2000

Fast Feature Selection by Analyzing Class Regions Approximated by Ellipsoids.

Kota Kawaratani, Shigeo Abe

Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks, 2000

Generalization Improvement of a Fuzzy Classifier with Pyramidal Membership Functions.

Shigeo Abe

Proceedings of the 15th International Conference on Pattern Recognition, 2000

1999

Function approximation based on fuzzy rules extracted from partitioned numerical data.

Ruck Thawonmas, Shigeo Abe

IEEE Trans. Syst. Man Cybern. Part B, 1999

A fuzzy classifier with ellipsoidal regions for diagnosis problems.

Shigeo Abe, Ruck Thawonmas, Masahiro Kayama

IEEE Trans. Syst. Man Cybern. Part C, 1999

Fuzzy function approximators with ellipsoidal regions.

Shigeo Abe

IEEE Trans. Syst. Man Cybern. Part B, 1999

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

Hisashi Tamaki, Etsuo Nishino, Shigeo Abe

Proceedings of the 1999 Congress on Evolutionary Computation, 1999

1998

Feature selection by analyzing class regions approximated by ellipsoids.

Shigeo Abe, Ruck Thawonmas, Yoshiki Kobayashi

IEEE Trans. Syst. Man Cybern. Part C, 1998

Dynamic cluster generation for a fuzzy classifier with ellipsoidal regions.

Shigeo Abe

IEEE Trans. Syst. Man Cybern. Part B, 1998

Rule acquisition based on hyperbox representation and its applications.

Ruck Thawonmas, Shigeo Abe

Proceedings of the Knowledge-Based Intelligent Electronic Systems, 1998

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

Shigeo Abe

Proceedings of the Knowledge-Based Intelligent Electronic Systems, 1998

1997

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

Ruck Thawonmas, Shigeo Abe

IEEE Trans. Syst. Man Cybern. Part B, 1997

A fuzzy classifier with ellipsoidal regions.

Shigeo Abe, Ruck Thawonmas

IEEE Trans. Fuzzy Syst., 1997

Function Approximation with Partitioned Ellipsoidal Regions.

Ruck Thawonmas, Shigeo Abe

Proceedings of the Progress in Connectionist-Based Information Systems: Proceedings of the 1997 International Conference on Neural Information Processing and Intelligent Information Systems, 1997

1996

Convergence acceleration of the Hopfield neural network by optimizing integration step sizes.

Shigeo Abe

IEEE Trans. Syst. Man Cybern. Part B, 1996

LSI module placement using the kohonen network.

Takeshi Inoue, Shigeo Abe, Masahiro Kayama

Syst. Comput. Jpn., 1996

Extraction of Fuzzy Rules for Classification Based on Partitioned Hyperboxes.

Ruck Thawonmas, Shigeo Abe

J. Intell. Fuzzy Syst., 1996

Tuning of a fuzzy classifier derived from data.

Shigeo Abe, Ming-Shong Lan, Ruck Thawonmas

Int. J. Approx. Reason., 1996

A fuzzy classifier based on partitioned hyperboxes.

Ruck Thawonmas, Shigeo Abe

Proceedings of International Conference on Neural Networks (ICNN'96), 1996

1995

A neural-network-based fuzzy classifier.

Volkmar Uebele, Shigeo Abe, Ming-Shong Lan

IEEE Trans. Syst. Man Cybern., 1995

Fuzzy rules extraction directly from numerical data for function approximation.

Shigeo Abe, Ming-Shong Lan

IEEE Trans. Syst. Man Cybern., 1995

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

Shigeo Abe, Ming-Shong Lan

IEEE Trans. Fuzzy Syst., 1995

Fuzzy Systems with Learning Capability.

Shigeo Abe

Proceedings of the Fuzzy Logic in Artificial Intelligence, 1995

Feature reduction based on analysis of fuzzy regions.

Ruck Thawonmas, Shigeo Abe

Proceedings of International Conference on Neural Networks (ICNN'95), Perth, WA, Australia, November 27, 1995

1994

Training neural net classifier to improve generalization capability.

Masahiro Kayama, Shigeo Abe

Syst. Comput. Jpn., 1994

1993

Extracting algorithms from pattern classification neural networks.

Shigeo Abe, Masahiro Kayama, Hiroshi Takenaga, Tadaaki Kitamura

Neural Networks, 1993

1992

Solving inequality constrained combinatorial optimization problems by the hopfield neural networks.

Shigeo Abe, Junzo Kawakami, Kotaro Hirasawa

Neural Networks, 1992

1990

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

Hiroshi Takenaga, Shigeo Abe, Masao Takatoo, Masahiro Kayama, Tadaaki Kitamura, Yosiyuki Okuyama

Proceedings of IAPR Workshop on Machine Vision Applications, 1990

Convergence of the Hopfield neural networks with inequality constraints.

Shigeo Abe

Proceedings of the IJCNN 1990, 1990

Learning by parallel forward propagation.

Shigeo Abe

Proceedings of the IJCNN 1990, 1990

1988

Instruction Architecture for a High Performance Integrated Prolog Processor IPP.

Ken-ichi Kurosawa, S. Yamaguchi, Shigeo Abe, Tadaaki Bandoh

Proceedings of the Logic Programming, 1988

1987

High Performance Integrated Prolog Processor IPP.

Shigeo Abe, Tadaaki Bandoh, S. Yamaguchi, Ken-ichi Kurosawa, Kaori Kiriyama

Proceedings of the 14th Annual International Symposium on Computer Architecture. Pittsburgh, 1987

1986

A New Optimization Technique for a Prolog Computer.

Shigeo Abe, Ken-ichi Kurosawa, Kaori Kiriyama

Proceedings of the Spring COMPCON'86, 1986

1982

Preliminary Performance Evaluation of Data Flow Computers.

Shigeo Abe, Ryosei Hiraoka, Yasushi Fukunaga, Tadaaki Bandoh, Kotaro Hirasawa, Yukio Kawamoto

Proceedings of the COMPCON'82, 1982