Presentation	2003/9/22 Local Linearized Least Squares Algorithm based on Penalty Function Method Masahiro YOSHIDA, Hiroshi NINOMIYA, Hideki ASAI,
PDF Download Page	PDF download Page Link
Abstract(in Japanese)	(See Japanese page)
Abstract(in English)	In this research, we introduce the local training method for feedforward neural networks. The local trainingmethods improve the computational complexity compared to the global training methods. Our method also can improve thecomputational complexity, because of learning for each neuron in neural networks. In addition, the proposed training method yields results with high convergence rates by using penalty term which is derived from Hessian matrix of the cost functionwith respect to weight.
Keyword(in Japanese)	(See Japanese page)
Keyword(in English)	Feedforward Neural Networks / Local Training / Penalty Function / Recursive Least Squares
Paper #	MLP2003-63
Date of Issue

Conference Information
Committee	NLP
Conference Date	2003/9/22(1days)
Place (in Japanese)	(See Japanese page)
Place (in English)
Topics (in Japanese)	(See Japanese page)
Topics (in English)
Chair
Vice Chair
Secretary
Assistant

Paper Information
Registration To	Nonlinear Problems (NLP)
Language	JPN
Title (in Japanese)	(See Japanese page)
Sub Title (in Japanese)	(See Japanese page)
Title (in English)	Local Linearized Least Squares Algorithm based on Penalty Function Method
Sub Title (in English)
Keyword(1)	Feedforward Neural Networks
Keyword(2)	Local Training
Keyword(3)	Penalty Function
Keyword(4)	Recursive Least Squares
1st Author's Name	Masahiro YOSHIDA
1st Author's Affiliation	Department of Systems Engineering, Shizuoka University()
2nd Author's Name	Hiroshi NINOMIYA
2nd Author's Affiliation	Dept. of Inf. Science, Shonan Institute of Technology
3rd Author's Name	Hideki ASAI
3rd Author's Affiliation	Department of Systems Engineering, Shizuoka University
Date	2003/9/22
Paper #	MLP2003-63
Volume (vol)	vol.103
Number (no)	335
Page	pp.pp.-
#Pages	6
Date of Issue