A hybrid learning method for multilayer neural networks

Author

Wang, Xin

Author_Institution

Dept. of Electr. Eng. & Appl. Phys., Oregon Grad. Inst. of Sci. & Technol., Beaverton, OR, USA

fYear

1993

fDate

6-9 Sep 1993

Firstpage

14

Lastpage

21

Abstract

A Newton learning approach for training a multilayer neural network is provided based on an efficient derivation of Hessian matrix of the network. Since the Newton´s method converges almost quadratically, the convergence performance is improved. A hybrid learning method is developed in conjunction with the conventional backpropagation algorithm. Its performance is demonstrated by the classical XOR and parity problems

Keywords

Hessian matrices; Newton method; backpropagation; convergence of numerical methods; learning (artificial intelligence); multilayer perceptrons; Hessian matrix; Newton learning approach; XOR problem; almost quadratic convergence; backpropagation algorithm; hybrid learning method; multilayer neural networks; parity problems; Acceleration; Backpropagation algorithms; Computational efficiency; Convergence; Information processing; Learning systems; Multi-layer neural network; Neural networks; Numerical stability; Physics;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks for Processing [1993] III. Proceedings of the 1993 IEEE-SP Workshop

Conference_Location

Linthicum Heights, MD

Print_ISBN

0-7803-0928-6

Type

conf

DOI

10.1109/NNSP.1993.471888

Filename

471888