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Shigeo Abe: [Publications] [Author Rank by year] [Co-authors] [Prefers] [Cites] [Cited by]

Publications of Author

  1. Shigeo Abe, R. Hiraoka, Y. Fukunaga, Tadaaki Bandoh, Kotaro Hirasawa, Yukio Kawamoto
    Preliminary Performance Evaluation of Data Flow Computers. [Citation Graph (0, 0)][DBLP]
    COMPCON, 1982, pp:224-227 [Conf]
  2. Shigeo Abe, Ken-ichi Kurosawa, Kaori Kiriyama
    A New Optimization Technique for a Prolog Computer. [Citation Graph (0, 0)][DBLP]
    COMPCON, 1986, pp:241-245 [Conf]
  3. Shigeo Abe
    Fuzzy LP-SVMs for Multiclass Problems. [Citation Graph (0, 0)][DBLP]
    ESANN, 2004, pp:429-434 [Conf]
  4. Shigeo Abe
    Modified backward feature selection by cross validation. [Citation Graph (0, 0)][DBLP]
    ESANN, 2005, pp:163-168 [Conf]
  5. Shigeo Abe, Takuya Inoue
    Fuzzy support vector machines for multiclass problems. [Citation Graph (0, 0)][DBLP]
    ESANN, 2002, pp:113-118 [Conf]
  6. Shigeo Abe, Keita Sakaguchi
    Generalization Improvement of a Fuzzy Classifier With Ellipsodial Regions. [Citation Graph (0, 0)][DBLP]
    FUZZ-IEEE, 2001, pp:207-210 [Conf]
  7. Shigeo Abe
    Training of Support Vector Machines with Mahalanobis Kernels. [Citation Graph (0, 0)][DBLP]
    ICANN (2), 2005, pp:571-576 [Conf]
  8. Shigeo Abe, Takuya Inoue
    Fast Training of Support Vector Machines by Extracting Boundary Data. [Citation Graph (0, 0)][DBLP]
    ICANN, 2001, pp:308-313 [Conf]
  9. Masamichi Ashihara, Shigeo Abe
    Feature Selection Based on Kernel Discriminant Analysis. [Citation Graph (0, 0)][DBLP]
    ICANN (2), 2006, pp:282-291 [Conf]
  10. Ken-ichi Kurosawa, S. Yamaguchi, Shigeo Abe, Tadaaki Bandoh
    Instruction Architecture for a High Performance Integrated Prolog Processor IPP. [Citation Graph (0, 0)][DBLP]
    ICLP/SLP, 1988, pp:1506-1530 [Conf]
  11. Shigeo Abe
    Generalization Improvement of a Fuzzy Classifier with Pyramidal Membership Functions. [Citation Graph (0, 0)][DBLP]
    ICPR, 2000, pp:2211-2214 [Conf]
  12. Shigeo Abe
    Fuzzy Systems with Learning Capability. [Citation Graph (0, 0)][DBLP]
    Fuzzy Logic in Artificial Intelligence, 1995, pp:101-115 [Conf]
  13. Kota Kawaratani, Shigeo Abe
    Fast Feature Selection by Analyzing Class Regions Approximated by Ellipsoids. [Citation Graph (0, 0)][DBLP]
    IJCNN (3), 2000, pp:549-554 [Conf]
  14. Hiroyasu Kubota, Hisashi Tamaki, Shigeo Abe
    Robust Function Approximation Using Fuzzy Rules with Ellipsoidal Regions. [Citation Graph (0, 0)][DBLP]
    IJCNN (6), 2000, pp:529-534 [Conf]
  15. Naoki Tsuchiya, Seiichi Ozawa, Shigeo Abe
    Training Three-Layer Neural Network Classifiers by Solving Inequalities. [Citation Graph (0, 0)][DBLP]
    IJCNN (3), 2000, pp:555-560 [Conf]
  16. Shigeo Abe, Tadaaki Bandoh, S. Yamaguchi, Ken-ichi Kurosawa, Kaori Kiriyama
    High Performance Integrated Prolog Processor IPP. [Citation Graph (0, 0)][DBLP]
    ISCA, 1987, pp:100-107 [Conf]
  17. Yuya Kamada, Shigeo Abe
    Support Vector Regression Using Mahalanobis Kernels. [Citation Graph (0, 0)][DBLP]
    ANNPR, 2006, pp:144-152 [Conf]
  18. Shinya Katagiri, Shigeo Abe
    Incremental Training of Support Vector Machines Using Truncated Hypercones. [Citation Graph (0, 0)][DBLP]
    ANNPR, 2006, pp:153-164 [Conf]
  19. Yusuke Torii, Shigeo Abe
    Fast Training of Linear Programming Support Vector Machines Using Decomposition Techniques. [Citation Graph (0, 0)][DBLP]
    ANNPR, 2006, pp:165-176 [Conf]
  20. Kohei Asano, Motohide Yoshimura, Shigeo Abe
    Detection of Cell Forms in Multicellular Objects. [Citation Graph (0, 0)][DBLP]
    CIMCA/IAWTIC, 2005, pp:793-798 [Conf]
  21. Noriaki Kawamura, Motohide Yoshimura, Shigeo Abe
    Image Query by Multiresolution Spectral Histograms. [Citation Graph (0, 0)][DBLP]
    CIMCA/IAWTIC, 2005, pp:660-665 [Conf]
  22. Takashi Iwai, Motohide Yoshimura, Shigeo Abe
    Detection of Protein Crystallizations under Dynamic Environment. [Citation Graph (0, 0)][DBLP]
    CIMCA/IAWTIC, 2005, pp:1121-1127 [Conf]
  23. Shosuke Kimura, Seiichi Ozawa, Shigeo Abe
    Incremental Kernel PCA for Online Learning of Feature Space. [Citation Graph (0, 0)][DBLP]
    CIMCA/IAWTIC, 2005, pp:595-600 [Conf]
  24. Shigeo Abe, Ming-Shong Lan, Ruck Thawonmas
    Tuning of a fuzzy classifier derived from data. [Citation Graph (0, 0)][DBLP]
    Int. J. Approx. Reasoning, 1996, v:14, n:1, pp:1-24 [Journal]
  25. Kenichi Kaieda, Shigeo Abe
    KPCA-based training of a kernel fuzzy classifier with ellipsoidal regions. [Citation Graph (0, 0)][DBLP]
    Int. J. Approx. Reasoning, 2004, v:37, n:3, pp:189-217 [Journal]
  26. Shigeo Abe, Junzo Kawakami, Kotaro Hirasawa
    Solving inequality constrained combinatorial optimization problems by the hopfield neural networks. [Citation Graph (0, 0)][DBLP]
    Neural Networks, 1992, v:5, n:4, pp:663-670 [Journal]
  27. Shigeo Abe, Masahiro Kayama, Hiroshi Takenaga, Tadaaki Kitamura
    Extracting algorithms from pattern classification neural networks. [Citation Graph (0, 0)][DBLP]
    Neural Networks, 1993, v:6, n:5, pp:729-735 [Journal]
  28. Seiichi Ozawa, Soon Lee Toh, Shigeo Abe, Shaoning Pang, Nikola Kasabov
    Incremental learning of feature space and classifier for face recognition. [Citation Graph (0, 0)][DBLP]
    Neural Networks, 2005, v:18, n:5-6, pp:575-584 [Journal]
  29. Daisuke Tsujinishi, Shigeo Abe
    Fuzzy least squares support vector machines for multiclass problems. [Citation Graph (0, 0)][DBLP]
    Neural Networks, 2003, v:16, n:5-6, pp:785-792 [Journal]
  30. Tomonori Kikuchi, Shigeo Abe
    Comparison between error correcting output codes and fuzzy support vector machines. [Citation Graph (0, 0)][DBLP]
    Pattern Recognition Letters, 2005, v:26, n:12, pp:1937-1945 [Journal]
  31. Shinya Katagiri, Shigeo Abe
    Incremental training of support vector machines using hyperspheres. [Citation Graph (0, 0)][DBLP]
    Pattern Recognition Letters, 2006, v:27, n:13, pp:1495-1507 [Journal]
  32. Shigeo Abe, Ruck Thawonmas, Masahiro Kayama
    A fuzzy classifier with ellipsoidal regions for diagnosis problems. [Citation Graph (0, 0)][DBLP]
    IEEE Transactions on Systems, Man, and Cybernetics, Part C, 1999, v:29, n:1, pp:140-148 [Journal]
  33. Ruck Thawonmas, Shigeo Abe
    A novel approach to feature selection based on analysis of class regions. [Citation Graph (0, 0)][DBLP]
    IEEE Transactions on Systems, Man, and Cybernetics, Part B, 1997, v:27, n:2, pp:196-207 [Journal]
  34. Ruck Thawonmas, Shigeo Abe
    Function approximation based on fuzzy rules extracted from partitioned numerical data. [Citation Graph (0, 0)][DBLP]
    IEEE Transactions on Systems, Man, and Cybernetics, Part B, 1999, v:29, n:4, pp:525-534 [Journal]
  35. Ryota Hosokawa, Shigeo Abe
    Fuzzy Classifiers Based on Kernel Discriminant Analysis. [Citation Graph (0, 0)][DBLP]
    ICANN (2), 2007, pp:180-189 [Conf]
  36. Shigeo Abe, Kenta Onishi
    Sparse Least Squares Support Vector Regressors Trained in the Reduced Empirical Feature Space. [Citation Graph (0, 0)][DBLP]
    ICANN (2), 2007, pp:527-536 [Conf]
  37. Shigeo Abe
    Sparse least squares support vector training in the reduced empirical feature space. [Citation Graph (0, 0)][DBLP]
    Pattern Anal. Appl., 2007, v:10, n:3, pp:203-214 [Journal]

  38. Optimizing kernel parameters by second-order methods. [Citation Graph (, )][DBLP]


  39. Comparison of sparse least squares support vector regressors trained in primal and dual. [Citation Graph (, )][DBLP]


  40. Batch Support Vector Training Based on Exact Incremental Training. [Citation Graph (, )][DBLP]


  41. Is Primal Better Than Dual. [Citation Graph (, )][DBLP]


  42. Convergence Improvement of Active Set Training for Support Vector Regressors. [Citation Graph (, )][DBLP]


  43. Improved Parameter Tuning Algorithms for Fuzzy Classifiers. [Citation Graph (, )][DBLP]


  44. An Efficient Incremental Kernel Principal Component Analysis for Online Feature Selection. [Citation Graph (, )][DBLP]


  45. Backward Varilable Selection of Support Vector Regressors by Block Deletion. [Citation Graph (, )][DBLP]


  46. Implementing Multi-class Classifiers by One-class Classification Methods. [Citation Graph (, )][DBLP]


  47. An Incremental Learning Algorithm of Ensemble Classifier Systems. [Citation Graph (, )][DBLP]


  48. Sparse support vector machines trained in the reduced empirical feature space. [Citation Graph (, )][DBLP]


  49. Feature selection based on kernel discriminant analysis for multi-class problems. [Citation Graph (, )][DBLP]


  50. Training of a fuzzy classifier with ellipsoidal regions by dynamic cluster generation. [Citation Graph (, )][DBLP]


  51. Rule acquisition based on hyperbox representation and its applications. [Citation Graph (, )][DBLP]


  52. A Fast Incremental Kernel Principal Component Analysis for Online Feature Extraction. [Citation Graph (, )][DBLP]


  53. Sparse Least Squares Support Vector Machines by Forward Selection Based on Linear Discriminant Analysis. [Citation Graph (, )][DBLP]


  54. Evaluation of Feature Selection by Multiclass Kernel Discriminant Analysis. [Citation Graph (, )][DBLP]


  55. Advanced Image Retrieval Using Multi-resolution Image Content. [Citation Graph (, )][DBLP]


  56. Optimal Input Selection of Neural Networks by Sensitivity Analysis and Its Application to Image Recognition. [Citation Graph (, )][DBLP]


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