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Estimation of Boolean Factor Analysis Performance by Informational Gain
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SYSNO ASEP 0335028 Document Type C - Proceedings Paper (int. conf.) R&D Document Type Conference Paper Title Estimation of Boolean Factor Analysis Performance by Informational Gain Title Ocenění efektivnosti booleovské faktorové analýzy pomocí informačního zisku Author(s) Frolov, A. (RU)
Húsek, Dušan (UIVT-O) RID, SAI, ORCID
Polyakov, P.Y. (RU)Source Title Advances in Intelligent Web Mastering - 2. - Berlin : Springer, 2010 / Snášel V. ; Szczepaniak P.S. ; Abraham A. ; Kacprzyk J. - ISBN 978-3-642-10686-6 Pages s. 83-94 Number of pages 12 s. Action AWIC 2009. Atlantic Web Intelligence Conference /6./ Event date 09.09.2009-11.09.2009 VEvent location Prague Country CZ - Czech Republic Event type WRD Language eng - English Country DE - Germany Keywords Boolean factor analysis ; informational gain ; Hopfield-like network Subject RIV IN - Informatics, Computer Science CEZ AV0Z10300504 - UIVT-O (2005-2011) UT WOS 000281727500008 EID SCOPUS 84865119533 DOI 10.1007/978-3-642-10687-3_8 Annotation To evaluate the soundness of multidimensional binary signal analysis based on Boolean factor analysis theory and mainly of its neural network implementation, proposed is a universal measure - informational gain. This measure is derived using classical informational theory results. Neural network based Boolean factor analysis method efficiency is demonstrated using this measure, both when applied to Bars Problem benchmark data and to real textual data. It is shown that when applied to the well defined Bars Problem data, Boolean factor analysis provides informational gain close to its maximum, i.e. the latent structure of the testing images data was revealed with the maximal accuracy. For scientific origin real textual data the informational gain provided by the method happened to be much higher comparing to that based on human experts proposal. Workplace Institute of Computer Science Contact Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Year of Publishing 2010
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