DocumentCode
436579
Title
A Hessian matrix approach for training nonlinear networks
Author
Yu, Changhua ; Manry, M.T.
Author_Institution
Dept. of Electr. Eng., Texas Univ., Arlington, TX, USA
Volume
2
fYear
2004
fDate
31 Aug.-4 Sept. 2004
Firstpage
1514
Abstract
In the original output weight optimization-hidden weight optimization (OWO-HWO) algorithm for training multilayer perceptions, only first order information is used to construct the desired net function. This gradient-like strategy inevitably reduces efficiency. In this paper, an efficient Hessian matrix inversion method is proposed for the hidden weights optimization. Numerical results validate the improvement of this algorithm.
Keywords
Hessian matrices; gradient methods; learning (artificial intelligence); matrix inversion; multilayer perceptrons; optimisation; Hessian matrix approach; gradient-like strategy; multilayer perception; nonlinear network training; output weight optimization-hidden weight optimization algorithm; Convergence; Delay; Joining processes; Multilayer perceptrons; Nonlinear equations; Optimization methods; Remote sensing; Signal processing; Signal processing algorithms; Training data;
fLanguage
English
Publisher
ieee
Conference_Titel
Signal Processing, 2004. Proceedings. ICSP '04. 2004 7th International Conference on
Print_ISBN
0-7803-8406-7
Type
conf
DOI
10.1109/ICOSP.2004.1441615
Filename
1441615
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