Modified stochastic approximation to enhance unsupervised learning
- 1 December 1977
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
Abstract
By simple modifications of a decision-directed learning procedure, the regression curves of multidimensional stochastic approximation can be rotated further apart, leading to enhanced convergence properties. Results of a Monte Carlo simulation for a binary hypotheses testing problem are given which illustrates this faster convergence.Keywords
This publication has 0 references indexed in Scilit: