Hidemitsu Ogawa - researchr alias

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Viewing Publication 1 - 44 from 44

2009

What Can We See behind Sampling Theorems?Hidemitsu Ogawa. ieicet, 92-A(3):688-695, 2009. [doi]

2008

Optimal noise suppression: A geometric nature of pseudoframes for subspacesShidong Li, Hidemitsu Ogawa. adcm, 28(2):141-155, 2008. [doi]

2006

Modulated Hebb-Oja learning Rule-a method for principal subspace analysisMarko Jankovic, Hidemitsu Ogawa. tnn, 17(2):345-356, 2006. [doi]

Constructing Kernel Functions for Binary RegressionMasashi Sugiyama, Hidemitsu Ogawa. ieicet, 89-D(7):2243-2249, 2006. [doi]

2005

Optimal Sampling Operator for Signal Restoration in the Presence of Signal Space and Observation Space NoisesAqeel Syed, Hidemitsu Ogawa. ieicet, 88-D(12):2828-2838, 2005. [doi]

2004

Time-oriented hierarchical method for computation of principal components using subspace learning algorithmMarko Jankovic, Hidemitsu Ogawa. IJNS, 14(5):313-323, 2004. [doi]

Characterization and Implementation of Partial Projection Filter in the Presence of Signal Space NoiseAqeel Syed, Hidemitsu Ogawa. ieicet, 87-D(12):2837-2844, 2004. [doi]

Partial Projection Filter for Signal Restoration in the Presence of Signal Space NoiseAqeel Syed, Hidemitsu Ogawa. ieicet, 87-D(12):2828-2836, 2004. [doi]

2003

A New Modulated Hebbian Learning Rule - Biologically Plausible Method for Local Computation of a Principal SubspaceMarko Jankovic, Hidemitsu Ogawa. IJNS, 13(4):215-223, 2003. [doi]

Radial basis function classifier for fault diagnosticsGanka Petkova Kovacheva, Hidemitsu Ogawa. isict 2003: 64-69 [doi]

2002

2001

A family of projection learningsAkira Hirabayashi, Hidemitsu Ogawa. scjapan, 32(5):21-35, 2001. [doi]

Error correcting memorization learning for noisy training examplesAkiko Nakashima, Akira Hirabayashi, Hidemitsu Ogawa. NN, 14(1):79-92, 2001. [doi]

Noise suppression in training examples for improving generalization capabilityAkiko Nakashima, Hidemitsu Ogawa. NN, 14(4-5):459-469, 2001. [doi]

Incremental projection learning for optimal generalizationMasashi Sugiyama, Hidemitsu Ogawa. NN, 14(1):53-66, 2001. [doi]

Properties of incremental projection learningMasashi Sugiyama, Hidemitsu Ogawa. NN, 14(1):67-78, 2001. [doi]

Incremental Active Learning for Optimal GeneralizationMasashi Sugiyama, Hidemitsu Ogawa. neco, 12(12):2909-2940, 2001.

Subspace Information Criterion for Model SelectionMasashi Sugiyama, Hidemitsu Ogawa. neco, 13(8):1863-1889, 2001.

2000

A new information criterion for the selection of subspace modelsMasashi Sugiyama, Hidemitsu Ogawa. esann 2000: 69-74 [doi]

Incremental Active Learning with Bias ReductionMasashi Sugiyama, Hidemitsu Ogawa. ijcnn 2000: 15-20 [doi]

1999

What can memorization learning do?Akira Hirabayashi, Hidemitsu Ogawa. ijcnn 1999: 659-662 [doi]

Training Data Selection for Optimal Generalization in Trigonometric Polynomial NetworksMasashi Sugiyama, Hidemitsu Ogawa. nips 2000: 624-630 [doi]

RKHS-based functional analysis for exact incremental learningSethu Vijayakumar, Hidemitsu Ogawa. ijon, 29(1-3):85-113, 1999. [doi]

1998

Relative Karhunen-Loeve transform method for pattern recognitionYukihiko Yamashita, Yasuyuki Ikeno, Hidemitsu Ogawa. icpr 1998: 1031-1033 [doi]

1996

Admissibility of memorization learning with respect to projection learning in the presence of noiseAkira Hirabayashi, Hidemitsu Ogawa. icnn 1996: 335-340 [doi]

1995

A unified theory of the family of projection filters for signal and image estimationYuji Koide, Yukihiko Yamashita, Hidemitsu Ogawa. scjapan, 26(4):95-105, 1995. [doi]

Pattern recognition in the presence of noiseD. Liu, Yukihiko Yamashita, Hidemitsu Ogawa. PR, 28(7):989-995, 1995. [doi]

A functional analytic approach to incremental learning in optimally generalizing neural networksSethu Vijayakumar, Hidemitsu Ogawa. icnn 1995: 777-782 [doi]

1994

Methods for choosing a training set which prevents over-learningKazutaka Yamasaki, Hidemitsu Ogawa. scjapan, 25(11):88-98, 1994. [doi]

1993

1992

Karhunen-Loeve SubspaceHidemitsu Ogawa. icpr 1992: 75-78 [doi]

Properties of averaged projection filter for image restorationYukihiko Yamashita, Hidemitsu Ogawa. scjapan, 23(1):69-78, 1992. [doi]

Image restoration by averaged projection filterYukihiko Yamashita, Hidemitsu Ogawa. scjapan, 23(1):79-88, 1992. [doi]

1989

A method of extracting a kinematically characteristic point from the 3-dimensional motion of a rigid bodyToyohiko Hayashi, Hidemitsu Ogawa, Taizo Iijima. scjapan, 20(7):72-82, 1989. [doi]

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 PathToyohiko Hayashi, Hidemitsu Ogawa, Taizo Iijima. scjapan, 20(9):1-12, 1989. [doi]

1986

A unified approach to generalized sampling theoremsHidemitsu Ogawa. icassp 1986: 1657-1660 [doi]

Parametric projection filter for image and signal restorationErkki Oja, Hidemitsu Ogawa. tsp, 34(6):1643-1653, 1986. [doi]

1969

Optimum Spatial Filter and UncertaintyHidemitsu Ogawa, Yoshinori Isomichi. iandc, 14(2):180-216, February 1969.

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