A neural model for category learning.

Abstract:

:We present a general neural model for supervised learning of pattern categories which can resolve pattern classes separated by nonlinear, essentially arbitrary boundaries. The concept of a pattern class develops from storing in memory a limited number of class elements (prototypes). Associated with each prototype is a modifiable scalar weighting factor (lambda) which effectively defines the threshold for categorization of an input with the class of the given prototype. Learning involves (1) commitment of prototypes to memory and (2) adjustment of the various lambda factors to eliminate classification errors. In tests, the model ably defined classification boundaries that largely separated complicated pattern regions. We discuss the role which divisive inhibition might play in a possible implementation of the model by a network of neurons.

journal_name

Biol Cybern

journal_title

Biological cybernetics

authors

Reilly DL,Cooper LN,Elbaum C

doi

10.1007/BF00387211

subject

Has Abstract

pub_date

1982-01-01 00:00:00

pages

35-41

issue

1

eissn

0340-1200

issn

1432-0770

journal_volume

45

pub_type

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