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Improved Fuzzy and Neural Network Algorithms for Word Frequency Prediction in Document Filtering
Peter Baranyi*, Laszlo T. Koczy**, and Tamas D. Gedeon***
*Computer and Automation Institute Hungarian Academy of Sciences and Dept. of Automation Technical University of Budapest, Budafoki u. 8, Budapest, H-1111, Hungary
**Dept. of Telecommunication and Telematics, Technical University of Budapest, Sztoczek u.2, Budapest, H- 1111, Hungary
***Dept. of Information Engineering, School of Computer Science and Engineering, The University of New South Wales, Sydney 2052 Australia
Received:October 10, 1997Accepted:January 25, 1998Published:June 20, 1998
Keywords:Information retrieval, Neural network, Fuzzy logic, Parameter set reduction
Abstract
With very large document collections or high-volume document streams of , finding relevant documents is a major information filtering problem. An aid to information retrieval systems produces a word frequency measure estimated from important parts of the document using neural network approaches. In this paper, a fuzzy logic technique and, as its simplified case, a neural network algorithm are proposed for this task. The comparison of these two and an alternative neural network algorithm are discussed.
Cite this article as:P. Baranyi, L. Koczy, and T. Gedeon, “Improved Fuzzy and Neural Network Algorithms for Word Frequency Prediction in Document Filtering,” J. Adv. Comput. Intell. Intell. Inform., Vol.2 No.3, pp. 88-95, 1998.Data files:
