Katsuyuki Hagiwara - researchr alias

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Viewing Publication 1 - 26 from 26

2023

On Gradient Descent Training Under Data Augmentation with On-Line Noisy CopiesKatsuyuki Hagiwara. ieicetd, 106(9):1537-1545, September 2023. [doi]

2022

Bridging between Soft and Hard Thresholding by ScalingKatsuyuki Hagiwara. ieicetd, 105-D(9):1529-1536, September 2022. [doi]

2019

A Model Selection Criterion for LASSO Estimate with ScalingKatsuyuki Hagiwara. iconip 2019: 248-259 [doi]

2018

On a Fitting of a Heaviside Function by Deep ReLU Neural NetworksKatsuyuki Hagiwara. iconip 2018: 59-69 [doi]

2017

A Scaling and Non-Negative Garrote in Soft-ThresholdingKatsuyuki Hagiwara. ieicet, 100-D(11):2702-2710, 2017. [doi]

2016

A Problem in Model Selection of LASSO and Introduction of ScalingKatsuyuki Hagiwara. iconip 2016: 20-27 [doi]

On scaling of soft-thresholding estimatorKatsuyuki Hagiwara. ijon, 194:360-371, 2016. [doi]

2014

Least Angle Regression in Orthogonal CaseKatsuyuki Hagiwara. iconip 2014: 540-547 [doi]

A Sparse Modeling Method Based on Reduction of Cost Function in Regularized Forward SelectionKatsuyuki Hagiwara. ieicet, 97-D(1):98-106, 2014. [doi]

2011

Nonparametric Regression Method Based on Orthogonalization and ThresholdingKatsuyuki Hagiwara. ieicet, 94-D(8):1610-1619, 2011. [doi]

2010

On a training scheme based on orthogonalization and thresholding for a nonparametric regression problemKatsuyuki Hagiwara. ijcnn 2010: 1-8 [doi]

2008

Orthogonalization and Thresholding Method for a Nonparametric Regression ProblemKatsuyuki Hagiwara. iconip 2009: 187-194 [doi]

Relation between weight size and degree of over-fitting in neural network regressionKatsuyuki Hagiwara, Kenji Fukumizu. NN, 21(1):48-58, 2008. [doi]

2007

Orthogonal Shrinkage Methods for Nonparametric Regression under Gaussian NoiseKatsuyuki Hagiwara. iconip 2008: 537-546 [doi]

2006

On the Expected Prediction Error of Orthogonal Regression with Variable ComponentsKatsuyuki Hagiwara, Hiroshi Ishitani. ieicet, 89-A(12):3699-3709, 2006. [doi]

2003

Model Selection with Componentwise Shrinkage in Orthogonal RegressionKatsuyuki Hagiwara. ieiceta, 86-A(7):1749-1758, 2003. [doi]

2002

Regularization learning, early stopping and biased estimatorKatsuyuki Hagiwara. ijon, 48(1-4):937-955, 2002. [doi]

On the Problem in Model Selection of Neural Network Regression in Overrealizable ScenarioKatsuyuki Hagiwara. neco, 14(8):1979-2002, 2002. [doi]

2001

Upper bound of the expected training error of neural network regression for a Gaussian noise sequenceKatsuyuki Hagiwara, Taichi Hayasaka, Naohiro Toda, Shiro Usui, Kazuhiro Kuno. NN, 14(10):1419-1429, 2001. [doi]

2000

Regularization Learning and Early Stopping in Linear NetworksKatsuyuki Hagiwara, Kazuhiro Kuno. ijcnn 2000: 511-516 [doi]

On the Problem in Model Selection of Neural Network Regression in Overrealizable ScenarioKatsuyuki Hagiwara, Kazuhiro Kuno, Shiro Usui. ijcnn 2000: 461-466 [doi]

1998

Upper Bounds on the Expected Training Errors of Neural Networks Regressions for a Gaussian NoiseKatsuyuki Hagiwara, Kazuhiro Kuno, Shiro Usui. iconip 1998: 502-505

1997

On the Model Selection for Linear Combination of Step-Type Basis FunctionsTaichi Hayasaka, Katsuyuki Hagiwara, Naohiro Toda, Shiro Usui. iconip 1998: 245-248

1996

An analysis of carp cerebellar eeg under the microgravitational environmentShiro Usui, Yutaka Hirata, Katsuyuki Hagiwara, Naohiro Toda, Ichiro Akagi. scjapan, 27(3):71-83, 1996. [doi]

1995

Analysis of the error back-propagation learning algorithms with gainQi Jia, Katsuyuki Hagiwara, Shiro Usui, Naohiro Toda. scjapan, 26(6):49-58, 1995. [doi]

1994

Equivalence relation between the back propagation learning process of an FNN and that of an FNNGQi Jia, Katsuyuki Hagiwara, Naohiro Toda, Shiro Usui. NN, 7(2):411, 1994. [doi]

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