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ORCIDHidemitsu Ogawa
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2000 – 2009
- 2009
[j34]Hidemitsu Ogawa:
What Can We See behind Sampling Theorems? IEICE Trans. Fundam. Electron. Commun. Comput. Sci. 92-A(3): 688-695 (2009)- 2008
- [j33]Shidong Li, Hidemitsu Ogawa:
Optimal noise suppression: A geometric nature of pseudoframes for subspaces. Adv. Comput. Math. 28(2): 141-155 (2008) - 2006
- [j32]Masashi Sugiyama, Hidemitsu Ogawa:
Constructing Kernel Functions for Binary Regression. IEICE Trans. Inf. Syst. 89-D(7): 2243-2249 (2006) - [j31]Marko Jankovic
, Hidemitsu Ogawa:
Modulated Hebb-Oja learning Rule-a method for principal subspace analysis. IEEE Trans. Neural Networks 17(2): 345-356 (2006) - 2005
- [j30]Aqeel Syed, Hidemitsu Ogawa:
Optimal Sampling Operator for Signal Restoration in the Presence of Signal Space and Observation Space Noises. IEICE Trans. Inf. Syst. 88-D(12): 2828-2838 (2005) - 2004
- [j29]Aqeel Syed, Hidemitsu Ogawa:
Partial Projection Filter for Signal Restoration in the Presence of Signal Space Noise. IEICE Trans. Inf. Syst. 87-D(12): 2828-2836 (2004) - [j28]Aqeel Syed, Hidemitsu Ogawa:
Characterization and Implementation of Partial Projection Filter in the Presence of Signal Space Noise. IEICE Trans. Inf. Syst. 87-D(12): 2837-2844 (2004) - [j27]Marko Jankovic, Hidemitsu Ogawa:
Time-oriented hierarchical method for computation of principal components using subspace learning algorithm. Int. J. Neural Syst. 14(5): 313-323 (2004) - 2003
- [j26]Marko Jankovic, Hidemitsu Ogawa:
A New Modulated Hebbian Learning Rule - Biologically Plausible Method for Local Computation of a Principal Subspace. Int. J. Neural Syst. 13(4): 215-223 (2003) - [c12]Ganka Petkova Kovacheva, Hidemitsu Ogawa:
Radial basis function classifier for fault diagnostics. ISICT 2003: 64-69 - 2002
- [j25]Masashi Sugiyama, Hidemitsu Ogawa:
Theoretical and Experimental Evaluation of the Subspace Information Criterion. Mach. Learn. 48(1-3): 25-50 (2002) - [j24]Masashi Sugiyama, Hidemitsu Ogawa:
Optimal design of regularization term and regularization parameter by subspace information criterion. Neural Networks 15(3): 349-361 (2002) - [j23]Hidekazu Iwaki, Hidemitsu Ogawa, Akira Hirabayashi:
Optimally generalizing neural networks with the ability to recover from single stuck-at r faults. Syst. Comput. Jpn. 33(7): 114-123 (2002) - [j22]Masashi Sugiyama, Hidemitsu Ogawa:
A unified method for optimizing linear image restoration filters. Signal Process. 82(11): 1773-1787 (2002) - 2001
- [j21]Masashi Sugiyama, Hidemitsu Ogawa:
Incremental Active Learning for Optimal Generalization. Neural Comput. 12(12): 2909-2940 (2001) - [j20]Masashi Sugiyama, Hidemitsu Ogawa:
Subspace Information Criterion for Model Selection. Neural Comput. 13(8): 1863-1889 (2001) - [j19]Masashi Sugiyama, Hidemitsu Ogawa:
Incremental projection learning for optimal generalization. Neural Networks 14(1): 53-66 (2001) - [j18]Masashi Sugiyama, Hidemitsu Ogawa:
Properties of incremental projection learning. Neural Networks 14(1): 67-78 (2001) - [j17]Akiko Nakashima, Akira Hirabayashi, Hidemitsu Ogawa:
Error correcting memorization learning for noisy training examples. Neural Networks 14(1): 79-92 (2001) - [j16]Akiko Nakashima, Hidemitsu Ogawa:
Noise suppression in training examples for improving generalization capability. Neural Networks 14(4-5): 459-469 (2001) - [j15]Akira Hirabayashi, Hidemitsu Ogawa:
A family of projection learnings. Syst. Comput. Jpn. 32(5): 21-35 (2001) - 2000
- [c11]Masashi Sugiyama, Hidemitsu Ogawa:
A new information criterion for the selection of subspace models. ESANN 2000: 69-74 - [c10]Masashi Sugiyama, Hidemitsu Ogawa:
Incremental Active Learning with Bias Reduction. IJCNN (1) 2000: 15-20
1990 – 1999
- 1999
- [j14]Sethu Vijayakumar, Hidemitsu Ogawa:
RKHS-based functional analysis for exact incremental learning. Neurocomputing 29(1-3): 85-113 (1999) - [c9]Akira Hirabayashi, Hidemitsu Ogawa:
What can memorization learning do? IJCNN 1999: 659-662 - [c8]Masashi Sugiyama, Hidemitsu Ogawa:
Training Data Selection for Optimal Generalization in Trigonometric Polynomial Networks. NIPS 1999: 624-630 - 1998
- [c7]Yukihiko Yamashita, Yasuyuki Ikeno, Hidemitsu Ogawa:
Relative Karhunen-Loeve transform method for pattern recognition. ICPR 1998: 1031-1033 - 1996
- [j13]Yukihiko Yamashita, Hidemitsu Ogawa:
Relative Karhunen-Loève transform. IEEE Trans. Signal Process. 44(2): 371-378 (1996) - [c6]Akira Hirabayashi, Hidemitsu Ogawa:
Admissibility of memorization learning with respect to projection learning in the presence of noise. ICNN 1996: 335-340 - 1995
- [j12]D. Liu, Yukihiko Yamashita, Hidemitsu Ogawa:
Pattern recognition in the presence of noise. Pattern Recognit. 28(7): 989-995 (1995) - [j11]Yuji Koide, Yukihiko Yamashita, Hidemitsu Ogawa:
A unified theory of the family of projection filters for signal and image estimation. Syst. Comput. Jpn. 26(4): 95-105 (1995) - [c5]Sethu Vijayakumar, Hidemitsu Ogawa:
A functional analytic approach to incremental learning in optimally generalizing neural networks. ICNN 1995: 777-782 - 1994
- [j10]Kazutaka Yamasaki, Hidemitsu Ogawa:
Methods for choosing a training set which prevents over-learning. Syst. Comput. Jpn. 25(11): 88-98 (1994) - [c4]Yukihiko Yamashita, Hidemitsu Ogawa:
Relative Karhunen-Loeve operator. ICPR (3) 1994: 168-170 - 1993
- [j9]Yukihiko Yamashita, Hidemitsu Ogawa:
Optimum image restoration and topological invariance. Syst. Comput. Jpn. 24(5): 53-63 (1993) - [j8]Yukihiko Yamashita, Hidemitsu Ogawa:
Optimum image restoration filters and generalized inverses of operators. Syst. Comput. Jpn. 24(6): 72-81 (1993) - [j7]Yukihiko Yamashita, Hidemitsu Ogawa:
Mutual relations among optimum image restoration filters. Syst. Comput. Jpn. 24(6): 82-92 (1993) - [c3]Kazutaka Yamasaki, Hidemitsu Ogawa:
A theory of over-learning in the presence of noise. ICNN 1993: 485-488 - 1992
- [j6]Yukihiko Yamashita, Hidemitsu Ogawa:
Properties of averaged projection filter for image restoration. Syst. Comput. Jpn. 23(1): 69-78 (1992) - [j5]Yukihiko Yamashita, Hidemitsu Ogawa:
Image restoration by averaged projection filter. Syst. Comput. Jpn. 23(1): 79-88 (1992) - [c2]Hidemitsu Ogawa:
Karhunen-Loeve Subspace. ICPR (2) 1992: 75-78
1980 – 1989
- 1989
- [j4]Toyohiko Hayashi, Hidemitsu Ogawa, Taizo Iijima:
A method of extracting a kinematically characteristic point from the 3-dimensional motion of a rigid body. Syst. Comput. Jpn. 20(7): 72-82 (1989) - [j3]Toyohiko Hayashi, Hidemitsu Ogawa, Taizo Iijima:
Estimation of the Reciprocating Point of a Rigid Body Moving Periodically in a Two-Dimensional Space - An Iterative Method Using a Correspondence between the Forward Path and the Backward Path. Syst. Comput. Jpn. 20(9): 1-12 (1989) - 1986
- [j2]Erkki Oja, Hidemitsu Ogawa:
Parametric projection filter for image and signal restoration. IEEE Trans. Acoust. Speech Signal Process. 34(6): 1643-1653 (1986) - [c1]Hidemitsu Ogawa:
A unified approach to generalized sampling theorems. ICASSP 1986: 1657-1660
1960 – 1969
- 1969
- [j1]Hidemitsu Ogawa, Yoshinori Isomichi:
Optimum Spatial Filter and Uncertainty. Inf. Control. 14(2): 180-216 (1969)
Coauthor Index
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