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010   $a3-319-30515-8
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035   $a(DE-He213)978-3-319-30515-8
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100   $a20160502d2016      u|         0
101 0 $aeng
135   $aurnn|008mamaa
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aSearch Techniques in Intelligent Classification Systems /$fby Andrey V. Savchenko
205   $a1st ed. 2016.
210  1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2016.
215   $a1 online resource (XIII, 82 p. 28 illus., 19 illus. in color.) 
225 1 $aSpringerBriefs in Optimization,$x2190-8354
311   $a3-319-30513-1 
320   $aIncludes bibliographical references at the end of each chapters.
327   $a1.Intelligent Classification Systems -- 2. Statistical Classification of Audiovisual Data -- 3. Hierarchical Intelligent Classification Systems -- 4. Approximate Nearest Neighbor Search in Intelligent Classification Systems -- 5. Search in Voice Control Systems -- 6. Conclusion. .
330   $aA unified methodology for categorizing various complex objects is presented in this book. Through probability theory, novel asymptotically minimax criteria suitable for practical applications in imaging and data analysis are examined including the special cases such as the Jensen-Shannon divergence and the probabilistic neural network. An optimal approximate nearest neighbor search algorithm, which allows faster classification of databases is featured. Rough set theory, sequential analysis and granular computing are used to improve performance of the hierarchical classifiers. Practical examples in face identification (including deep neural networks), isolated commands recognition in voice control system and classification of visemes captured by the Kinect depth camera are included. This approach creates fast and accurate search procedures by using exact probability densities of applied dissimilarity measures. This book can be used as a guide for independent study and as supplementary material for a technically oriented graduate course in intelligent systems and data mining. Students and researchers interested in the theoretical and practical aspects of intelligent classification systems will find answers to: - Why conventional implementation of the naive Bayesian approach does not work well in image classification? - How to deal with insufficient performance of hierarchical classification systems? - Is it possible to prevent an exhaustive search of the nearest neighbor in a database?
410  0$aSpringerBriefs in Optimization,$x2190-8354
606   $aMathematical optimization
606   $aPattern recognition
606   $aMachinery
606   $aSystem theory
606   $aPotential theory (Mathematics)
606   $aOptimization$3https://scigraph.springernature.com/ontologies/product-market-codes/M26008
606   $aPattern Recognition$3https://scigraph.springernature.com/ontologies/product-market-codes/I2203X
606   $aMachinery and Machine Elements$3https://scigraph.springernature.com/ontologies/product-market-codes/T17039
606   $aSystems Theory, Control$3https://scigraph.springernature.com/ontologies/product-market-codes/M13070
606   $aComplex Systems$3https://scigraph.springernature.com/ontologies/product-market-codes/M13090
606   $aPotential Theory$3https://scigraph.springernature.com/ontologies/product-market-codes/M12163
615  0$aMathematical optimization.
615  0$aPattern recognition.
615  0$aMachinery.
615  0$aSystem theory.
615  0$aPotential theory (Mathematics).
615 14$aOptimization.
615 24$aPattern Recognition.
615 24$aMachinery and Machine Elements.
615 24$aSystems Theory, Control.
615 24$aComplex Systems.
615 24$aPotential Theory.
676   $a005.74
700   $aSavchenko$b Andrey V$4aut$4http://id.loc.gov/vocabulary/relators/aut$0756076
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
906   $aBOOK
912   $a9910254072103321
996   $aSearch techniques in intelligent classification systems$91523621
997   $aUNINA