Kenji Nagata Kenji Nagata

Articles:  1-6 of 6

  • An exhaustive search and stability of sparse estimation for feature selection problem

    Kenji Nagata , Jun Kitazono , Shin-ichi Nakajima , Satoshi Eifuku , Ryoi Tamura , Masato Okada

    Feature selection problem has been widely used for various fields. In particular, the sparse estimation has the advantage that its computational cost is the polynomial order of the number of features. …

    IPSJ SIG Notes 2014-MPS-100(10), 1-6, 2014-09-18

  • Sparse Estimation of Spike-Triggered Average

    Shimpei Yotsukura , Toshiaki Omori , Kenji Nagata , Masato Okada

    The spike-triggered average (STA) and phase response curve characterize the response properties of single neurons. A recent theoretical study proposed a method to estimate the phase response curve by …

    情報処理学会論文誌数理モデル化と応用(TOM) 7(1), 15-21, 2014-03-28

    IPSJ 

  • A Numerical Analysis of Learning Coefficient in Radial Basis Function Network

    Satoru Tokuda , Kenji Nagata , Masato Okada

    The radial basis function (RBF) network is a regression model that uses the sum of radial basis functions such as Gaussian functions. It has recently been widely applied to spectral deconvolution such …

    情報処理学会論文誌数理モデル化と応用(TOM) 6(3), 117-123, 2013-12-27

    IPSJ 

  • Sparse Estimation of Spike-Triggered Average

    Shimpei Yotsukura , Toshiaki Omori , Kenji Nagata , Masato Okada

    The spike-triggered average (STA) and phase response curve characterize the response properties of single neurons. A recent theoretical study proposed a method to estimate the phase response curve by …

    IPSJ SIG Notes 2013-MPS-93(4), 1-6, 2013-05-16

  • A numerical analysis of learning coefficient in radial basis function network

    Satoru Tokuda , Kenji Nagata , Masato Okada

    The radial basis function (RBF) network is a regression model that use the sum of radial basis functions such as Gaussian functions. It has recently been widely applied to spectral deconvolution such …

    研究報告数理モデル化と問題解決(MPS) 2013-MPS-92(9), 1-6, 2013-02-20

  • Exhaustive Search of Feature Subsets for Support Vector Machine Classification

    Jun Kitazono , Kenji Nagata , Shinichi Nakajima , Akira Manda , Satoshi Eifuku , Ryoi Tamura , Masato Okada

    Feature selection in machine learning is an important process for improving the generalization capability and interpretability of learned models through the selection of a relevant feature subset. In …

    研究報告数理モデル化と問題解決(MPS) 2013-MPS-92(8), 1-6, 2013-02-20

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