A stochastic learning algorithm for generalization problems

Author

Ramamoorthy, C.V. ; Shekhar, Shashi

Author_Institution

Div. of Comput. Sci., California Univ., Berkeley, CA, USA

fYear

1989

fDate

22-24 Nov 1989

Firstpage

136

Lastpage

141

Abstract

A discussion is presented of the requirements of learning for generalization, which is NP-complete and cannot be addressed by traditional methods based on gradient descent. The authors present a stochastic learning algorithm based on simulated annealing in weight space and discuss stopping criteria for the algorithm, to avoid overfitting of learning examples

Keywords

learning systems; neural nets; simulated annealing; stochastic processes; NP-complete; generalization; generalization problems; simulated annealing; stochastic back propagation; stochastic backpropagation; stochastic learning algorithm; stopping criteria; weight space; Backpropagation algorithms; Computer science; Neural networks; Noise shaping; Predictive models; Shape; Simulated annealing; Speech recognition; Stochastic processes; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

TENCON '89. Fourth IEEE Region 10 International Conference

Conference_Location

Bombay

Type

conf

DOI

10.1109/TENCON.1989.176913

Filename

176913

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2835861