Content-Based Software Classification by Self-Organization
نویسنده
چکیده
This paper is concerned with a case study in content-based classification of textual documents. In particular we compare the application of two prominent self-organizing neural networks to the same problem domain, namely the organization of software libraries. The two models are Adaptive Resonance Theory and Self-Organizing Maps. As a result we are able to show that both models successfully arrange software components according to their semantic similarity.
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تاریخ انتشار 1995