Constructive Neural Network Learning Algorithms for Multi-Category Pattern Classification

Constructive learning algorithms o er an approach for incremental construction of potentially near-minimal neural network architectures for pattern classi cation 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 xed network architecture. Several such algorithms proposed in the literature have been shown to converge to zero classi cation errors (under certain assumptions) on a nite, non-contradictory training set in a 2-category classi cation problem. This paper explores multi-category extensions of several constructive neural network learning algorithms for pattern classi cation. In each case, we establish the convergence to zero classi cation errors on a multicategory classi cation 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 classi cation and also suggest several interesting directions for future research. This research was partially supported by the National Science Foundation grant IRI-9409580 to Vasant Honavar.