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Constructive Neural Network Learning Algorithms for Multi-Category Pattern Classification


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

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Abstract

Constructive Neural Network Learning Algorithms
for Multi-Category Pattern Classification
Rajesh Parekh, Jihoon Yang, and 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
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 linearly 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:All uncategorized technical reports
ID code:00000114
Deposited by:Staff Account on 07 December 1995



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