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

Paper:

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, 1997
Accepted:
January 25, 1998
Published:
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.
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