Constructive Neural Network Learning Algorithms for Multi-Category Pattern Classification

Parekh, Rajesh G., Yang, Jihoon and Honavar, Vasant G. (1995) Constructive Neural Network Learning Algorithms for Multi-Category Pattern Classification. Technical Report TR95-15, Department of Computer Science, Iowa State University.

Abstract

Constructive Neural Network Learning Algorithms for Multi-Category
Pattern Classification
Technical Report TR95-15
Rajesh Parekh, Jihoon Yang, and Vasant Honavar
This research was partially supported by the National Science Foundation grant
IRI-9409580 to Vasant Honavar.
Artificial Intelligence Research Group
Department of Computer Science
226 Atanasoff Hall,
Iowa State University,
Ames, IA 50011. U.S.A.
parekh|yang|honavar@cs.iastate.edu
Abstract
Constructive learning algorithms offer an approach for incremental
construction of potentially near-minimal neural network architectures for
pattern classification tasks. Such algorithms help overcome the need
for ad-hoc and often inappropriate choice of network topology in
the use of algorithms that search for a suitable weight setting in
an otherwise a-priori fixed network architecture.
Several such algorithms proposed in the
literature have been shown to converge to zero classification errors
(under certain assumptions)
on a finite, non-contradictory training set in a 2-category
classification problem.
This paper explores multi-category extensions of several constructive
neural network learning algorithms for pattern classification.
In each case, we establish the convergence to zero classification
errors on a multi-category classification task (under certain assumptions).
Results of experiments with non-separable multi-category data sets
demonstrate the feasibility of this approach to multi-category pattern
classification and also suggest several interesting directions for future
research.


Subjects:
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ID code:
00000113
Deposited by:
Staff Account on 09 October 1995





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