Efficient retrieval of information with regards to its meaning and content is an important problem in data mining systems for the creation, management and querying of very large information databases existing in the World Wide Web. In this paper we deal with the main aspect of the problem of content based retrieval, namely, with the problem of document classification, outlining a novel improved and systematic approach to it's solution. We present a document classification system for non-domain specific content based on the learning and generalization capabilities mainly of SVM neural networks. The main contribution of this paper lies on the feature extraction methodology which, first, involves word semantic categories and not raw words as other rival approaches. As a consequence of coping with the problem of dimensionality reduction, the proposed approach introduces a novel higher order approach for document categorization feature extraction by considering word semantic categories higher order correlation analysis, both two and three dimensional, based on cooccurrence analysis. The suggested methodology compares favourably to widely accepted, raw word frequency based techniques in a collection of documents concerning the Dewey Decimal Classification (DDC) system. In these comparisons different Multilayer Perceptrons (MLP) algorithms as well as the Support Vector Machine (SVM), the LVQ and the conventional k-NN technique are involved. SVM models seem to outperform all other rival methods in this study.
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