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020 _a9780857294951
_9978-0-85729-495-1
040 _cMX-MeUAM
050 4 _aQA75.5-76.95
082 0 4 _a004
_223
100 1 _aMurty, M. Narasimha.
_eauthor.
245 1 0 _aPattern Recognition
_h[recurso electrónico] :
_bAn Algorithmic Approach /
_cby M. Narasimha Murty, V. Susheela Devi.
250 _a1.
264 1 _aLondon :
_bSpringer London,
_c2011.
300 _aXII, 263p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aUndergraduate Topics in Computer Science,
_x1863-7310 ;
_v0
505 0 _aIntroduction -- Representation -- Nearest Neighbour Based Classifiers -- Bayes Classifier -- Hidden Markov Models -- Decision Trees -- Support Vector Machines -- Combination of Classifiers -- Clustering -- Summary -- An Application: Handwritten Digit Recognition.
520 _aObserving the environment, and recognising patterns for the purpose of decision-making, is fundamental to human nature. The scientific discipline of pattern recognition (PR) is devoted to how machines use computing to discern patterns in the real world. This must-read textbook provides an exposition of principal topics in PR using an algorithmic approach. Presenting a thorough introduction to the concepts of PR and a systematic account of the major topics, the text also reviews the vast progress made in the field in recent years. The algorithmic approach makes the material more accessible to computer science and engineering students. Topics and features: Makes thorough use of examples and illustrations throughout the text, and includes end-of-chapter exercises and suggestions for further reading Describes a range of classification methods, including nearest-neighbour classifiers, Bayes classifiers, and decision trees Includes chapter-by-chapter learning objectives and summaries, as well as extensive referencing Presents standard tools for machine learning and data mining, covering neural networks and support vector machines that use discriminant functions Explains important aspects of PR in detail, such as clustering Discusses hidden Markov models for speech and speaker recognition tasks, clarifying core concepts through simple examples This concise and practical text/reference will perfectly meet the needs of senior undergraduate and postgraduate students of computer science and related disciplines. Additionally, the book will be useful to all researchers who need to apply PR techniques to solve their problems. Dr. M. Narasimha Murty is a Professor in the Department of Computer Science and Automation at the Indian Institute of Science, Bangalore. Dr. V. Susheela Devi is a Senior Scientific Officer at the same institution.
650 0 _aComputer science.
650 1 4 _aComputer Science.
650 2 4 _aComputer Science, general.
700 1 _aDevi, V. Susheela.
_eauthor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9780857294944
830 0 _aUndergraduate Topics in Computer Science,
_x1863-7310 ;
_v0
856 4 0 _zLibro electrónico
_uhttp://148.231.10.114:2048/login?url=http://link.springer.com/book/10.1007/978-0-85729-495-1
596 _a19
942 _cLIBRO_ELEC
999 _c198451
_d198451