RIA: Constructive Neural Network Learning Algorithms for Pattern Classification

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9409580 Honavar The primary objectives of the proposed research is a systematic exploration of a class of constructive learning algorithms for neural networks for pattern classification that can potentially: 1) Eliminate the need for ad-hoc, often inappropriate choices of network architecture; 2) Yield close to minimal networks whose complexity is commensurate with the complexity of the particular task implicitly specified by the training data; 3) Provide empirical estimates of expected-case complexity of real- world pattern classification tasks; 4) Offer natural ways to trade off some performance measures (e.g., learning time) against others (e.g., network size); 5) Provide convenient ways to incorporate domain knowledge (when available) to speed up learning.