Adding noise to training -- A psychological perspective (Preprint)

[Mark Gluck]

In a recent paper we have discussed the role of stochastic noise
in training data for adaptive network models of human classification
learning.  We have shown how the incorporation of such
noise (usually modelled as a stochastic sampling process on the external
stimuli) improves generalization performance, especially with 
deterministic discriminations which underconstrain the set of possible
solution-weights. The addition of noise to the training biases the
network to find solutions (and generalizations) which more closely correspond
to the behavior of humans in psychological experiments.  The reference is:

Gluck, M. A. (1991,in press). Stimulus sampling and distributed representations      in adaptive network theories of learning. In A. Healy, S. Kosslyn, & R. Shiffrin (Eds.), Festschrift for W. K. Estes. New Jersey: Lawrence Erlbaum Associates.

Copies can be received by emailing to:
______________________________________________________________________

Dr. Mark A. Gluck
Center for Molecular & Behavioral Neuroscience
Rutgers University
197 University Ave.
Newark, New Jersey  07102

        Phone:  (201) 648-1080 (Ext. 3221)
        Fax:    (201) 648-1272
        Email:  gluck at pavlov.rutgers.edu