Stochastic correlative learning algorithms

Authors:
S. Haykin;Zhe Chen;S. Becker
Affiliations:
Commun. Res. Lab., McMaster Univ., Hamilton, Ont., Canada;-;-
Venue:
IEEE Transactions on Signal Processing
Year:
2004

Citing 0
Cited 5

The Cocktail Party Problem

Neural Computation
A Novel Model-Based Hearing Compensation Design Using a Gradient-Free Optimization Method

Neural Computation
Switching strategies for sequential decision problems with multiplicative loss with application to portfolios

IEEE Transactions on Signal Processing
A heuristically enhanced gradient approximation (HEGA) algorithm for training neural networks

Neurocomputing
Signal processing in a nonlinear, nongaussian, and nonstationary world

Nonlinear Speech Modeling and Applications

Quantified Score

Hi-index	35.69

Visualization

Abstract

This paper addresses stochastic correlative learning as the basis for a broadly defined class of statistical learning algorithms known collectively as the algorithm of pattern extraction (ALOPEX) family. Starting with the neurobiologically motivated Hebb's rule, the two conventional forms of the ALOPEX algorithm are derived, followed by a modified variant designed to improve the convergence speed. We next describe two more elaborate versions of the ALOPEX algorithm, which incorporate particle filtering that exemplifies a form of Monte Carlo simulation, to exchange computational complexity for an improved convergence and tracking behavior. In support of the different forms of the ALOPEX algorithm developed herein, we present three different experiments using synthetic and real-life data on binocular fusion of stereo images, on-line prediction, and system identification.