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Recurrent Neural Network Based Boolean Factor Analysis and its Application to Word Clustering
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SYSNO ASEP 0321649 Document Type J - Journal Article R&D Document Type Journal Article Subsidiary J Článek ve WOS Title Recurrent Neural Network Based Boolean Factor Analysis and its Application to Word Clustering Title Booleovská faktorová analýza založená na rekurentní neuronové síti a její aplikace na shlukování slov Author(s) Frolov, A. A. (RU)
Húsek, Dušan (UIVT-O) RID, SAI, ORCID
Polyakov, P.Y. (RU)Source Title IEEE Transactions on Neural Networks - ISSN 1045-9227
Roč. 20, č. 7 (2009), s. 1073-1086Number of pages 14 s. Language eng - English Country US - United States Keywords recurrent neural network ; Hopfield-like neural network ; associative memory ; unsupervised learning ; neural network architecture ; neural network application ; statistics ; Boolean factor analysis ; concepts search ; information retrieval Subject RIV BB - Applied Statistics, Operational Research R&D Projects 1M0567 GA MŠMT - Ministry of Education, Youth and Sports (MEYS) CEZ AV0Z10300504 - UIVT-O (2005-2011) UT WOS 000267941800002 EID SCOPUS 67949118777 DOI 10.1109/TNN.2009.2016090 Annotation Neural network based algorithm for word clustering as an extension of the neural network based Boolean factor analysis algorithm is introduced. Technique based on a Bayesian procedure has been developed to provide a complete description of factors in terms of component probability and to enhance the accuracy of classification of documents. Method is applied to two types of textual data on Neural Networks in two different languages. Workplace Institute of Computer Science Contact Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Year of Publishing 2010
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