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This paper introduces a method of optimising the radial basis function classifier representing a compromise between fast heuristic approach and a fully adaptive approch. The method is especially suitable when the size and complexity of the classification problem are such that large numbers of kernel functions are required to maximise generalisation (minimise error rate on test data). The problem examined here is the speaker-independent recognition of isolated utterances of the letteres of the alphabet, which is a difficult and useful task. Results are presented to illustrate the optimisation process, following which the performance of the optimised classifier is compared with several other neural and traditional techniques.


Conference paper

Publication Date



345 - 349