JACIII Vol.2 No.3 pp. 88-95
doi: 10.20965/jaciii.1998.p0088


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

October 10, 1997
January 25, 1998
June 20, 1998
Information retrieval, Neural network, Fuzzy logic, Parameter set reduction
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.
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