A Constructive Learning Algorithm for Text Categorization
The paper presents a new constructive learning algorithm CWSN (Covering With Sphere Neighborhoods) for three-layer neural networks, and uses it to solve the text categorization (TC) problem. The algorithm is based on a geometrical representation of M-P neuron, i.e., for each category, CWSN tries to find a set of sphere neighborhoods which cover as many positive documents as possible, and don’t cover any negative documents. Each sphere neighborhood represents a covering area in the vector space and it also corresponds to a hidden neuron in the network. The experimental results show that CWSN demonstrates promising performance compared to other commonly used TC classifiers.
KeywordsInput Vector Hide Neuron Text Categorization Neural Classifier Sphere Neighborhood
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