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.
Constructive Neural Network Learning Algorithms for Multi-Category
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.
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
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
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