K. Hagiwara

发表

On the problem of applying AIC to determine the structure of a layered feedforward neural network

Katsuyuki Hagiwara, Naohiro Toda, Shiro Usui, 1993, Proceedings of 1993 International Conference on Neural Networks (IJCNN-93-Nagoya, Japan).

Adaptive scaling for soft-thresholding estimator

K. Hagiwara, 2015, 1601.08002.

Over-fitting behavior of Gaussian unit under Gaussian noise

K. Fukumizu, K. Hagiwara, 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).

No . 858 November 07 , 2002 A general upper bound of likelihood ratio in binary regression

K. Fukumizu, K. Hagiwara, 2002 .

On the Asymptotic Distribution of the Least Squares Estimators for Non-indenti ed Models

S. Usui, K. Hagiwara, N. Toda, 2007 .

On the Problem in Model Selection of Neural Network Regression in Overrealizable Scenario

Katsuyuki Hagiwara, Shiro Usui, Kazuhiro Kuno, 2002, Neural Computation.

A consistent model selection for orthogonal regression under component-wise shrinkage

Katsuyuki Hagiwara, K. Hagiwara, 2006 .

On the Problem in Model Selection of Neural Network Regression in Overrealizable Scenario

S. Usui, K. Hagiwara, K. Kuno, 2000, IJCNN.

On gradient descent training under data augmentation with on-line noisy copies

K. Hagiwara, 2022, ArXiv.

Bridging between soft and hard thresholding by scaling

K. Hagiwara, 2021, IEICE Trans. Inf. Syst..

Least Angle Regression in Orthogonal Case

Katsuyuki Hagiwara, K. Hagiwara, 2014, ICONIP.

A general upper bound of likelihood ratio for regression

Kenji Fukumizu, Katsuyuki Hagiwara, K. Fukumizu, 2003 .

Prediction-accuracy improvement of neural network to ferromagnetic multilayers by Gaussian data augmentation and ensemble learning

K. Hagiwara, K. Nawa, Kohji Nakamura, 2023, Computational Materials Science.

Regularization learning, early stopping and biased estimator

Katsuyuki Hagiwara, K. Hagiwara, 2002, Neurocomputing.

Upper bound of the expected training error of neural network regression for a Gaussian noise sequence

Katsuyuki Hagiwara, Naohiro Toda, Shiro Usui, 2001, Neural Networks.

A Sparse Modeling Method Based on Reduction of Cost Function in Regularized Forward Selection

Katsuyuki Hagiwara, K. Hagiwara, 2014, IEICE Trans. Inf. Syst..

Determination of the number of hidden units from a statistical viewpoint

Katsuyuki Hagiwara, Naohiro Toda, Shiro Usui, 1999, ICONIP'99. ANZIIS'99 & ANNES'99 & ACNN'99. 6th International Conference on Neural Information Processing. Proceedings (Cat. No.99EX378).

Nonparametric Regression Method Based on Orthogonalization and Thresholding

Katsuyuki Hagiwara, K. Hagiwara, 2011, IEICE Trans. Inf. Syst..

On a training scheme based on orthogonalization and thresholding for a nonparametric regression problem

Katsuyuki Hagiwara, K. Hagiwara, 2010, The 2010 International Joint Conference on Neural Networks (IJCNN).

A Model Selection Criterion for LASSO Estimate with Scaling

Katsuyuki Hagiwara, K. Hagiwara, 2019, ICONIP.

On an improvement of LASSO by scaling pdf

Katsuyuki Hagiwara, K. Hagiwara, 2018, ArXiv.

A Scaling and Non-Negative Garrote in Soft-Thresholding

Katsuyuki Hagiwara, K. Hagiwara, 2017, IEICE Trans. Inf. Syst..

A Problem in Model Selection of LASSO and Introduction of Scaling

Katsuyuki Hagiwara, K. Hagiwara, 2016, ICONIP.

On scaling of soft-thresholding estimator

Katsuyuki Hagiwara, K. Hagiwara, 2016, Neurocomputing.

On the Statistical Properties of Regression Model Using Step-Type Basis Functions.

Katsuyuki Hagiwara, Naohiro Toda, Shiro Usui, 1997 .

On the training error and generalization error of neural network regression without identifiability

K. Hagiwara, 2007 .

On the Problem in Model Selection of Neural Network Regression in Overrealizable Scenario

Katsuyuki Hagiwara, K. Hagiwara, 2002, Neural Computation.