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＜電子ブック＞
Search Techniques in Intelligent Classification Systems / by Andrey V. Savchenko
(SpringerBriefs in Optimization. ISSN:2191575X)

版	1st ed. 2016.
出版者	(Cham : Springer International Publishing : Imprint: Springer)
出版年	2016
本文言語	英語
大きさ	XIII, 82 p. 28 illus., 19 illus. in color : online resource
著者標目	*Savchenko, Andrey V author SpringerLink (Online service)
件　名	LCSH:Mathematical optimization LCSH:Pattern recognition systems LCSH:Machinery LCSH:System theory LCSH:Control theory LCSH:Potential theory (Mathematics) FREE:Optimization FREE:Automated Pattern Recognition FREE:Machinery and Machine Elements FREE:Systems Theory, Control FREE:Complex Systems FREE:Potential Theory
一般注記	1.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. A 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? HTTP:URL=https://doi.org/10.1007/978-3-319-30515-8

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