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Shigeo Abe
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2020 – today
- 2024
- [c80]Shigeo Abe:
Evaluating Support Vector Machines with Multiple Kernels by Random Search. ANNPR 2024: 61-72 - [c79]Shigeo Abe:
Are Bounded Support Vectors Harmful to Stable Classification. IJCNN 2024: 1-7 - 2023
- [c78]Shigeo Abe:
Training Minimal Complexity Support Vector Machines with Multiple Kernels. SMC 2023: 913-919 - 2022
- [c77]Shigeo Abe:
Do Minimal Complexity Least Squares Support Vector Machines Work? ANNPR 2022: 53-64 - [c76]Shigeo Abe:
Soft Upper-bound Support Vector Machines. IJCNN 2022: 1-8 - 2021
- [c75]Shigeo Abe:
Soft Upper-bound Minimal Complexity LP SVMs. IJCNN 2021: 1-7 - 2020
- [j36]Shigeo Abe:
Minimal Complexity Support Vector Machines for Pattern Classification. Comput. 9(4): 88 (2020) - [c74]Shigeo Abe:
Minimal Complexity Support Vector Machines. ANNPR 2020: 89-101
2010 – 2019
- 2019
- [c73]Shigeo Abe
:
Analyzing Minimal Complexity Machines. IJCNN 2019: 1-8 - 2018
- [j35]Shigeo Abe
Are twin hyperplanes necessary? Pattern Recognit. Lett. 116: 218-224 (2018) - [c72]Shigeo Abe
Effect of Equality Constraints to Unconstrained Large Margin Distribution Machines. ANNPR 2018: 41-53 - 2017
- [j34]Shigeo Abe
Unconstrained large margin distribution machines. Pattern Recognit. Lett. 98: 96-102 (2017) - 2016
- [j33]Shigeo Abe
Fusing sequential minimal optimization and Newton's method for support vector training. Int. J. Mach. Learn. Cybern. 7(3): 345-364 (2016) - [c71]Shigeo Abe
Improving Generalization Abilities of Maximal Average Margin Classifiers. ANNPR 2016: 29-41 - 2015
- [j32]Shigeo Abe
Fuzzy support vector machines for multilabel classification. Pattern Recognit. 48(6): 2110-2117 (2015) - [c70]Shigeo Abe
Optimizing working sets for training support vector regressors by Newton's method. IJCNN 2015: 1-8 - 2014
- [c69]Shigeo Abe
Incremental Feature Selection by Block Addition and Block Deletion Using Least Squares SVRs. ANNPR 2014: 35-46 - [c68]Shigeo Abe
Incremental Input Variable Selection by Block Addition and Block Deletion. ICANN 2014: 547-554 - 2013
- [c67]Shigeo Abe
Feature Selection by Iterative Block Addition and Block Deletion. SMC 2013: 2677-2682 - 2012
- [c66]Takashi Nagatani, Shigeo Abe
Feature Selection by Block Addition and Block Deletion. ANNPR 2012: 48-59 - [c65]Shigeo Abe
Training Mahalanobis Kernels by Linear Programming. ICANN (2) 2012: 339-346 - 2011
- [c64]Shigeo Abe
Fast Support Vector Training by Newton's Method. ICANN (2) 2011: 143-150 - [c63]Takuya Kitamura, Shigeo Abe
Multiple Nonlinear Subspace Methods Using Subspace-based Support Vector Machines. ICMLA (1) 2011: 358-363 - [c62]Tao Ban, Changshui Zhang, Shigeo Abe
Mining interlacing manifolds in high dimensional spaces. SAC 2011: 942-949 - 2010
- [b2]Shigeo Abe:
Support Vector Machines for Pattern Classification. Advances in Pattern Recognition, Springer 2010, ISBN 978-1-84996-097-7, pp. i-xix, 1-471 - [j31]Takuya Kitamura, Shigeo Abe:
Subspace-Based L2 Support Vector Machines. Aust. J. Intell. Inf. Process. Syst. 12(3) (2010) - [j30]Takashi Nagatani, Seiichi Ozawa, Shigeo Abe:
Fast Variable Selection by Block Addition and Block Deletion. J. Intell. Learn. Syst. Appl. 2(4): 200-211 (2010) - [c61]Tsuneyoshi Ishii, Shigeo Abe
Evaluation of Feature Selection by Multiclass Kernel Discriminant Analysis. ANNPR 2010: 13-24 - [c60]Shigeo Abe:
Active set training of support vector regressors. ESANN 2010 - [c59]Shigeo Abe
Convergence Improvement of Active Set Training for Support Vector Regressors. ICANN (2) 2010: 1-10 - [c58]Yasuyuki Tajiri, Ryousuke Yabuwaki, Takuya Kitamura, Shigeo Abe
Feature Extraction Using Support Vector Machines. ICONIP (2) 2010: 108-115 - [c57]Takuya Kitamura, Syogo Takeuchi, Shigeo Abe
Feature selection and fast training of subspace based support vector machines. IJCNN 2010: 1-6 - [c56]Ryousuke Yabuwaki, Shigeo Abe
Convergence improvement of active set support vector training. IJCNN 2010: 1-5 - [c55]Seiichi Ozawa, Yohei Takeuchi, Shigeo Abe
A Fast Incremental Kernel Principal Component Analysis for Online Feature Extraction. PRICAI 2010: 487-497
2000 – 2009
- 2009
- [j29]Yusuke Torii, Shigeo Abe
Decomposition techniques for training linear programming support vector machines. Neurocomputing 72(4-6): 973-984 (2009) - [j28]Tao Ban, Changshui Zhang, Shigeo Abe
A new approach to discover interlacing data structures in high-dimensional space. J. Intell. Inf. Syst. 33(1): 3-22 (2009) - [j27]Kazuya Morikawa, Seiichi Ozawa, Shigeo Abe
Tuning membership functions of kernel fuzzy classifiers by maximizing margins. Memetic Comput. 1(3): 221-228 (2009) - [j26]Takuya Kitamura, Syogo Takeuchi, Shigeo Abe
Subspace-based support vector machines for pattern classification. Neural Networks 22(5-6): 558-567 (2009) - [c54]Kazuki Iwamura, Shigeo Abe:
Sparse support vector machines by kernel discriminant analysis. ESANN 2009 - [c53]Shigeo Abe
Is Primal Better Than Dual. ICANN (1) 2009: 854-863 - [c52]Tao Ban, Youki Kadobayashi, Shigeo Abe
Sparse kernel feature analysis using FastMap and its variants. IJCNN 2009: 256-263 - [c51]Takuya Kitamura, Shigeo Abe
Subspace based least squares support vector machines for pattern classification. IJCNN 2009: 1640-1646 - [c50]Shigenori Muraoka, Shigeo Abe
Sparse support vector regressors based on forward basis selection. IJCNN 2009: 2183-2187 - [c49]Syogo Takeuchi, Takuya Kitamura, Shigeo Abe
Subspace based linear programming support vector machines. IJCNN 2009: 3067-3073 - 2008
- [j25]Tsuneyoshi Ishii, Masamichi Ashihara, Shigeo Abe
Kernel discriminant analysis based feature selection. Neurocomputing 71(13-15): 2544-2552 (2008) - [c48]Shigeo Abe
Sparse Least Squares Support Vector Machines by Forward Selection Based on Linear Discriminant Analysis. ANNPR 2008: 54-65 - [c47]Shigeo Abe:
Comparison of sparse least squares support vector regressors trained in primal and dual. ESANN 2008: 469-474 - [c46]Shigeo Abe
Batch Support Vector Training Based on Exact Incremental Training. ICANN (1) 2008: 295-304 - [c45]Kazuya Morikawa, Shigeo Abe
Improved Parameter Tuning Algorithms for Fuzzy Classifiers. ICONIP (1) 2008: 937-944 - [c44]Kazuki Iwamura, Shigeo Abe
Sparse support vector machines trained in the reduced empirical feature space. IJCNN 2008: 2398-2404 - [c43]Tsuneyoshi Ishii, Shigeo Abe
Feature selection based on kernel discriminant analysis for multi-class problems. IJCNN 2008: 2455-2460 - 2007
- [j24]Shinji Kita, Seiichi Ozawa, Satoshi Maekawa, Shigeo Abe
A Learning Algorithm of Boosting Kernel Discriminant Analysis for Pattern Recognition. IEICE Trans. Inf. Syst. 90-D(11): 1853-1863 (2007) - [j23]Shigeo Abe
Sparse least squares support vector training in the reduced empirical feature space. Pattern Anal. Appl. 10(3): 203-214 (2007) - [c42]Shigeo Abe:
Optimizing kernel parameters by second-order methods. ESANN 2007: 259-264 - [c41]Ryota Hosokawa, Shigeo Abe
Fuzzy Classifiers Based on Kernel Discriminant Analysis. ICANN (2) 2007: 180-189 - [c40]Shigeo Abe
Sparse Least Squares Support Vector Regressors Trained in the Reduced Empirical Feature Space. ICANN (2) 2007: 527-536 - [c39]Takashi Nagatani, Shigeo Abe
Backward Varilable Selection of Support Vector Regressors by Block Deletion. IJCNN 2007: 2117-2122 - [c38]Yohei Takeuchi, Seiichi Ozawa, Shigeo Abe
An Efficient Incremental Kernel Principal Component Analysis for Online Feature Selection. IJCNN 2007: 2346-2351 - 2006
- [j22]Shinya Katagiri, Shigeo Abe
Incremental training of support vector machines using hyperspheres. Pattern Recognit. Lett. 27(13): 1495-1507 (2006) - [c37]Yuya Kamada, Shigeo Abe
Support Vector Regression Using Mahalanobis Kernels. ANNPR 2006: 144-152 - [c36]Shinya Katagiri, Shigeo Abe
Incremental Training of Support Vector Machines Using Truncated Hypercones. ANNPR 2006: 153-164 - [c35]Yusuke Torii, Shigeo Abe
Fast Training of Linear Programming Support Vector Machines Using Decomposition Techniques. ANNPR 2006: 165-176 - [c34]Masamichi Ashihara, Shigeo Abe
Feature Selection Based on Kernel Discriminant Analysis. ICANN (2) 2006: 282-291 - [c33]Tao Ban, Shigeo Abe:
Implementing Multi-class Classifiers by One-class Classification Methods. IJCNN 2006: 327-332 - [c32]Takuya Kidera, Seiichi Ozawa, Shigeo Abe
An Incremental Learning Algorithm of Ensemble Classifier Systems. IJCNN 2006: 3421-3427 - 2005
- [b1]Shigeo Abe:
Support Vector Machines for Pattern Classification. Advances in Pattern Recognition, Springer 2005, ISBN 978-1-85233-929-6, pp. I-XV, 1-343 - [j21]Seiichi Ozawa, Soon Lee Toh, Shigeo Abe
Incremental learning of feature space and classifier for face recognition. Neural Networks 18(5-6): 575-584 (2005) - [j20]Tomonori Kikuchi, Shigeo Abe
Comparison between error correcting output codes and fuzzy support vector machines. Pattern Recognit. Lett. 26(12): 1937-1945 (2005) - [c31]Shosuke Kimura, Seiichi Ozawa, Shigeo Abe:
Incremental Kernel PCA for Online Learning of Feature Space. CIMCA/IAWTIC 2005: 595-600 - [c30]Noriaki Kawamura, Motohide Yoshimura, Shigeo Abe:
Image Query by Multiresolution Spectral Histograms. CIMCA/IAWTIC 2005: 660-665 - [c29]Kohei Asano, Motohide Yoshimura, Shigeo Abe:
Detection of Cell Forms in Multicellular Objects. CIMCA/IAWTIC 2005: 793-798 - [c28]Takashi Iwai, Motohide Yoshimura, Shigeo Abe:
Detection of Protein Crystallizations under Dynamic Environment. CIMCA/IAWTIC 2005: 1121-1127 - [c27]Shigeo Abe:
Modified backward feature selection by cross validation. ESANN 2005: 163-168 - [c26]Shigeo Abe:
Training of Support Vector Machines with Mahalanobis Kernels. ICANN (2) 2005: 571-576 - [c25]Shinya Katagiri, Shigeo Abe:
Selecting Support Vector Candidates for Incremental Training. SMC 2005: 1258-1263 - 2004
- [j19]Kenichi Kaieda, Shigeo Abe
KPCA-based training of a kernel fuzzy classifier with ellipsoidal regions. Int. J. Approx. Reason. 37(3): 189-217 (2004) - [c24]Shigeo Abe:
Fuzzy LP-SVMs for Multiclass Problems. ESANN 2004: 429-434 - 2003
- [j18]Daisuke Tsujinishi, Shigeo Abe
Fuzzy least squares support vector machines for multiclass problems. Neural Networks 16(5-6): 785-792 (2003) - 2002
- [c23]Shigeo Abe, Takuya Inoue:
Fuzzy support vector machines for multiclass problems. ESANN 2002: 113-118 - [c22]Motohide Yoshimura, Hajime Kiyose, Shigeo Abe:
Advanced Image Retrieval Using Multi-resolution Image Content. MVA 2002: 330-333 - [c21]Shigeo Abe
Analysis of support vector machines. NNSP 2002: 89-98 - 2001
- [c20]Shigeo Abe, Keita Sakaguchi:
Generalization Improvement of a Fuzzy Classifier With Ellipsodial Regions. FUZZ-IEEE 2001: 207-210 - [c19]Shigeo Abe
Fast Training of Support Vector Machines by Extracting Boundary Data. ICANN 2001: 308-313 - 2000
- [c18]Shigeo Abe:
Generalization Improvement of a Fuzzy Classifier with Pyramidal Membership Functions. ICPR 2000: 2211-2214 - [c17]Hiroyasu Kubota, Hisashi Tamaki, Shigeo Abe
Robust Function Approximation Using Fuzzy Rules with Ellipsoidal Regions. IJCNN (6) 2000: 529-534 - [c16]Kota Kawaratani, Shigeo Abe
Fast Feature Selection by Analyzing Class Regions Approximated by Ellipsoids. IJCNN (3) 2000: 549-554 - [c15]Naoki Tsuchiya, Seiichi Ozawa, Shigeo Abe
Training Three-Layer Neural Network Classifiers by Solving Inequalities. IJCNN (3) 2000: 555-560
1990 – 1999
- 1999
- [j17]Shigeo Abe
A fuzzy classifier with ellipsoidal regions for diagnosis problems. IEEE Trans. Syst. Man Cybern. Part C 29(1): 140-148 (1999) - [j16]Ruck Thawonmas
Function approximation based on fuzzy rules extracted from partitioned numerical data. IEEE Trans. Syst. Man Cybern. Part B 29(4): 525-534 (1999) - [j15]Shigeo Abe
Fuzzy function approximators with ellipsoidal regions. IEEE Trans. Syst. Man Cybern. Part B 29(5): 654-661 (1999) - [c14]Hisashi Tamaki, Etsuo Nishino, Shigeo Abe
A genetic algorithm approach to multi-objective scheduling problems with earliness and tardiness penalties. CEC 1999: 46-52 - 1998
- [j14]Shigeo Abe
Feature selection by analyzing class regions approximated by ellipsoids. IEEE Trans. Syst. Man Cybern. Part C 28(2): 282-287 (1998) - [j13]Shigeo Abe
Dynamic cluster generation for a fuzzy classifier with ellipsoidal regions. IEEE Trans. Syst. Man Cybern. Part B 28(6): 869-876 (1998) - [c13]Ruck Thawonmas, Shigeo Abe:
Rule acquisition based on hyperbox representation and its applications. KES (1) 1998: 120-125 - [c12]Shigeo Abe:
Training of a fuzzy classifier with ellipsoidal regions by dynamic cluster generation. KES (1) 1998: 126-131 - 1997
- [j12]Shigeo Abe
A fuzzy classifier with ellipsoidal regions. IEEE Trans. Fuzzy Syst. 5(3): 358-368 (1997) - [j11]Ruck Thawonmas
A novel approach to feature selection based on analysis of class regions. IEEE Trans. Syst. Man Cybern. Part B 27(2): 196-207 (1997) - [c11]Ruck Thawonmas, Shigeo Abe:
Function Approximation with Partitioned Ellipsoidal Regions. ICONIP (1) 1997: 380-383 - 1996
- [j10]Shigeo Abe
Tuning of a fuzzy classifier derived from data. Int. J. Approx. Reason. 14(1): 1-24 (1996) - [j9]Ruck Thawonmas, Shigeo Abe:
Extraction of Fuzzy Rules for Classification Based on Partitioned Hyperboxes. J. Intell. Fuzzy Syst. 4(3): 215-226 (1996) - [j8]Takeshi Inoue, Shigeo Abe, Masahiro Kayama:
LSI module placement using the kohonen network. Syst. Comput. Jpn. 27(6): 92-105 (1996) - [j7]Shigeo Abe
Convergence acceleration of the Hopfield neural network by optimizing integration step sizes. IEEE Trans. Syst. Man Cybern. Part B 26(1): 194-201 (1996) - [c10]Ruck Thawonmas, Shigeo Abe:
A fuzzy classifier based on partitioned hyperboxes. ICNN 1996: 1097-1102 - 1995
- [j6]Shigeo Abe
A method for fuzzy rules extraction directly from numerical data and its application to pattern classification. IEEE Trans. Fuzzy Syst. 3(1): 18-28 (1995) - [j5]Shigeo Abe
Fuzzy rules extraction directly from numerical data for function approximation. IEEE Trans. Syst. Man Cybern. 25(1): 119-129 (1995) - [j4]Volkmar Uebele, Shigeo Abe
A neural-network-based fuzzy classifier. IEEE Trans. Syst. Man Cybern. 25(2): 353-361 (1995) - [c9]Ruck Thawonmas, Shigeo Abe:
Feature reduction based on analysis of fuzzy regions. ICNN 1995: 2130-2133 - [c8]Shigeo Abe
Fuzzy Systems with Learning Capability. Fuzzy Logic in Artificial Intelligence 1995: 101-115 - 1994
- [j3]Masahiro Kayama, Shigeo Abe:
Training neural net classifier to improve generalization capability. Syst. Comput. Jpn. 25(2): 101-110 (1994) - 1993
- [j2]Shigeo Abe, Masahiro Kayama, Hiroshi Takenaga, Tadaaki Kitamura:
Extracting algorithms from pattern classification neural networks. Neural Networks 6(5): 729-735 (1993) - 1992
- [j1]Shigeo Abe
Solving inequality constrained combinatorial optimization problems by the hopfield neural networks. Neural Networks 5(4): 663-670 (1992) - 1990
- [c7]Shigeo Abe
Learning by parallel forward propagation. IJCNN 1990: 99-104 - [c6]Shigeo Abe
Convergence of the Hopfield neural networks with inequality constraints. IJCNN 1990: 869-874 - [c5]Hiroshi Takenaga, Shigeo Abe, Masao Takatoo, Masahiro Kayama, Tadaaki Kitamura, Yosiyuki Okuyama:
Optimal Input Selection of Neural Networks by Sensitivity Analysis and Its Application to Image Recognition. MVA 1990: 117-120
1980 – 1989
- 1988
- [c4]Ken-ichi Kurosawa, S. Yamaguchi, Shigeo Abe, Tadaaki Bandoh:
Instruction Architecture for a High Performance Integrated Prolog Processor IPP. ICLP/SLP 1988: 1506-1530 - 1987
- [c3]Shigeo Abe
High Performance Integrated Prolog Processor IPP. ISCA 1987: 100-107 - 1986
- [c2]Shigeo Abe, Ken-ichi Kurosawa, Kaori Kiriyama:
A New Optimization Technique for a Prolog Computer. COMPCON 1986: 241-245 - 1982
- [c1]Shigeo Abe, Ryosei Hiraoka, Yasushi Fukunaga, Tadaaki Bandoh, Kotaro Hirasawa, Yukio Kawamoto:
Preliminary Performance Evaluation of Data Flow Computers. COMPCON 1982: 224-227
Coauthor Index
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last updated on 2024-09-30 21:56 CEST by the dblp team
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