Optimum Spatial Filter and Uncertainty - Hidemitsu Ogawa; Yoshinori Isomichi (Inf. Control., 1969)

A unified approach to generalized sampling theorems. - Hidemitsu Ogawa (ICASSP, 1986)

Parametric projection filter for image and signal restoration. - Erkki Oja; Hidemitsu Ogawa (IEEE Trans. Acoust. Speech Signal Process., 1986)

A method of extracting a kinematically characteristic point from the 3-dimensional motion of a rigid body. - Toyohiko Hayashi; Hidemitsu Ogawa; Taizo Iijima (Syst. Comput. Jpn., 1989)

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. - Toyohiko Hayashi; Hidemitsu Ogawa; Taizo Iijima (Syst. Comput. Jpn., 1989)

Karhunen-Loeve Subspace. - Hidemitsu Ogawa (ICPR (2), 1992)

Properties of averaged projection filter for image restoration. - Yukihiko Yamashita; Hidemitsu Ogawa (Syst. Comput. Jpn., 1992)

Image restoration by averaged projection filter. - Yukihiko Yamashita; Hidemitsu Ogawa (Syst. Comput. Jpn., 1992)

A theory of over-learning in the presence of noise. - Kazutaka Yamasaki; Hidemitsu Ogawa (ICNN, 1993)

Optimum image restoration and topological invariance. - Yukihiko Yamashita; Hidemitsu Ogawa (Syst. Comput. Jpn., 1993)

Optimum image restoration filters and generalized inverses of operators. - Yukihiko Yamashita; Hidemitsu Ogawa (Syst. Comput. Jpn., 1993)

Mutual relations among optimum image restoration filters. - Yukihiko Yamashita; Hidemitsu Ogawa (Syst. Comput. Jpn., 1993)

Relative Karhunen-Loeve operator. - Yukihiko Yamashita; Hidemitsu Ogawa (ICPR (3), 1994)

Methods for choosing a training set which prevents over-learning. - Kazutaka Yamasaki; Hidemitsu Ogawa (Syst. Comput. Jpn., 1994)

A functional analytic approach to incremental learning in optimally generalizing neural networks. - Sethu Vijayakumar; Hidemitsu Ogawa (ICNN, 1995)

Pattern recognition in the presence of noise. - D. Liu; Yukihiko Yamashita; Hidemitsu Ogawa (Pattern Recognit., 1995)

A unified theory of the family of projection filters for signal and image estimation. - Yuji Koide; Yukihiko Yamashita; Hidemitsu Ogawa (Syst. Comput. Jpn., 1995)

Admissibility of memorization learning with respect to projection learning in the presence of noise. - Akira Hirabayashi; Hidemitsu Ogawa (ICNN, 1996)

Relative Karhunen-Loève transform. - Yukihiko Yamashita; Hidemitsu Ogawa (IEEE Trans. Signal Process., 1996)

Relative Karhunen-Loeve transform method for pattern recognition. - Yukihiko Yamashita; Yasuyuki Ikeno; Hidemitsu Ogawa (ICPR, 1998)

What can memorization learning do? - Akira Hirabayashi; Hidemitsu Ogawa (IJCNN, 1999)

Training Data Selection for Optimal Generalization in Trigonometric Polynomial Networks. - Masashi Sugiyama; Hidemitsu Ogawa (NIPS, 1999)

RKHS-based functional analysis for exact incremental learning. - Sethu Vijayakumar; Hidemitsu Ogawa (Neurocomputing, 1999)

A new information criterion for the selection of subspace models. - Masashi Sugiyama; Hidemitsu Ogawa (ESANN, 2000)

Incremental Active Learning with Bias Reduction. - Masashi Sugiyama; Hidemitsu Ogawa (IJCNN (1), 2000)

Subspace Information Criterion for Model Selection. - Masashi Sugiyama; Hidemitsu Ogawa (Neural Comput., 2001)

Incremental Active Learning for Optimal Generalization. - Masashi Sugiyama; Hidemitsu Ogawa (Neural Comput., 2001)

Error correcting memorization learning for noisy training examples. - Akiko Nakashima; Akira Hirabayashi; Hidemitsu Ogawa (Neural Networks, 2001)

Noise suppression in training examples for improving generalization capability. - Akiko Nakashima; Hidemitsu Ogawa (Neural Networks, 2001)

Incremental projection learning for optimal generalization. - Masashi Sugiyama; Hidemitsu Ogawa (Neural Networks, 2001)

Properties of incremental projection learning. - Masashi Sugiyama; Hidemitsu Ogawa (Neural Networks, 2001)

A family of projection learnings. - Akira Hirabayashi; Hidemitsu Ogawa (Syst. Comput. Jpn., 2001)

Theoretical and Experimental Evaluation of the Subspace Information Criterion. - Masashi Sugiyama; Hidemitsu Ogawa (Mach. Learn., 2002)

Optimal design of regularization term and regularization parameter by subspace information criterion. - Masashi Sugiyama; Hidemitsu Ogawa (Neural Networks, 2002)

Optimally generalizing neural networks with the ability to recover from single stuck-at r faults. - Hidekazu Iwaki; Hidemitsu Ogawa; Akira Hirabayashi (Syst. Comput. Jpn., 2002)

A unified method for optimizing linear image restoration filters. - Masashi Sugiyama; Hidemitsu Ogawa (Signal Process., 2002)

Radial basis function classifier for fault diagnostics. - Ganka Petkova Kovacheva; Hidemitsu Ogawa (ISICT, 2003)

A New Modulated Hebbian Learning Rule - Biologically Plausible Method for Local Computation of a Principal Subspace. - Marko Jankovic; Hidemitsu Ogawa (Int. J. Neural Syst., 2003)

Partial Projection Filter for Signal Restoration in the Presence of Signal Space Noise. - Aqeel Syed; Hidemitsu Ogawa (IEICE Trans. Inf. Syst., 2004)

Characterization and Implementation of Partial Projection Filter in the Presence of Signal Space Noise. - Aqeel Syed; Hidemitsu Ogawa (IEICE Trans. Inf. Syst., 2004)

Time-oriented hierarchical method for computation of principal components using subspace learning algorithm. - Marko Jankovic; Hidemitsu Ogawa (Int. J. Neural Syst., 2004)

Optimal Sampling Operator for Signal Restoration in the Presence of Signal Space and Observation Space Noises. - Aqeel Syed; Hidemitsu Ogawa (IEICE Trans. Inf. Syst., 2005)

Constructing Kernel Functions for Binary Regression. - Masashi Sugiyama; Hidemitsu Ogawa (IEICE Trans. Inf. Syst., 2006)

Modulated Hebb-Oja learning Rule-a method for principal subspace analysis. - Marko Jankovic; Hidemitsu Ogawa (IEEE Trans. Neural Networks, 2006)

Optimal noise suppression: A geometric nature of pseudoframes for subspaces. - Shidong Li; Hidemitsu Ogawa (Adv. Comput. Math., 2008)

What Can We See behind Sampling Theorems? - Hidemitsu Ogawa (IEICE Trans. Fundam. Electron. Commun. Comput. Sci., 2009)