Title :
Content-based software classification by self-organization
Author_Institution :
Inst. of Software Technol., Wien Univ., Austria
Abstract :
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
Keywords :
ART neural nets; file organisation; self-organising feature maps; software engineering; software libraries; adaptive resonance theory neural nets; content-based software classification; self-organizing maps; semantic similarity; software library organization; textual documents; Application software; Artificial neural networks; Neural networks; Organizing; Prototypes; Resonance; Software libraries; Software performance; Software reusability; Subspace constraints;
Conference_Titel :
Neural Networks, 1995. Proceedings., IEEE International Conference on
Conference_Location :
Perth, WA
Print_ISBN :
0-7803-2768-3
DOI :
10.1109/ICNN.1995.487573
