03461nam a22003855i 4500001001800000003000900018005001700027007001500044008004100059020001800100020001900118024003500137082001200172100003400184245011800218250000700336264003800343300003300381336002600414337002600440338003600466347002400502490006200526505026800588520185900856650002202715650002202737650003102759700003202790710003402822773002002856776003602876830006202912856010102974978-0-85729-495-1DE-He21320260521092040.0cr nn 008mamaa110525s2011    xxk|    s    |||| 0|eng d  a9780857294951  a997808572949517 a10.1007/978-0-85729-495-12doi04a0042231 aMurty, M. Narasimha.eauthor.10aPattern Recognitionh[electronic resource] :bAn Algorithmic Approach /cby M. Narasimha Murty, V. Susheela Devi.  a1. 1aLondon :bSpringer London,c2011.  aXII, 263p.bonline resource.  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier  atext filebPDF2rda1 aUndergraduate Topics in Computer Science,x1863-7310 ;v00 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.  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. 0aCOMPUTER SCIENCE.14aCOMPUTER SCIENCE.24aCOMPUTER SCIENCE, GENERAL.1 aDevi, V. Susheela.eauthor.2 aSpringerLink (Online service)0 tSpringer eBooks08iPrinted edition:z9780857294944 0aUndergraduate Topics in Computer Science,x1863-7310 ;v040uhttp://dx.doi.org/10.1007/978-0-85729-495-1zVer el texto completo en las instalaciones del CICY