Abstract
This paper aims at developing techniqus for design and implementation of neural classifiers. Based on our previous study on generalized RBF neural network architecture and learning criterion function for parameter optimization, this work addresses two realization issues, i.e. supervised input features selection and genetic computation techniques for tuning classifiers. A comparative study on classifiation performance is carried on by a set of protein sequence data.
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