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020 _a9780857294951
_99780857294951
024 7 _a10.1007/9780857294951
_2doi
035 _avtls000333905
039 9 _a201509030242
_bVLOAD
_c201404130556
_dVLOAD
_c201404092345
_dVLOAD
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040 _aMX-SnUAN
_bspa
_cMX-SnUAN
_erda
050 4 _aQA75.5-76.95
100 1 _aMurty, M. Narasimha.
_eautor
_9306305
245 1 0 _aPattern Recognition :
_bAn Algorithmic Approach /
_cby M. Narasimha Murty, V. Susheela Devi.
250 _a1.
264 1 _aLondon :
_bSpringer London,
_c2011.
300 _axii, 263 páginas
_brecurso en línea.
336 _atexto
_btxt
_2rdacontent
337 _acomputadora
_bc
_2rdamedia
338 _arecurso en línea
_bcr
_2rdacarrier
347 _aarchivo de texto
_bPDF
_2rda
490 0 _aUndergraduate Topics in Computer Science,
_x1863-7310 ;
_v0
500 _aSpringer eBooks
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.
590 _aPara consulta fuera de la UANL se requiere clave de acceso remoto.
700 1 _aDevi, V. Susheela.
_eautor
_9306306
710 2 _aSpringerLink (Servicio en línea)
_9299170
776 0 8 _iEdición impresa:
_z9780857294944
856 4 0 _uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/978-0-85729-495-1
_zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
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999 _c280994
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