000			03005nam a2200277Ia 4500
003			OSt
005			20250717121826.0
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020			_a9780470682289
040			_aCSL _beng _cCSL
041			_aeng
084			_aB28930bD65,8(B) Q1 TB _qCSL
100			_aWebb, Andrew R. _eauthor.
245		0	_aStatistical Pattern Recognition
250			_a3rd ed.
260			_aWest Sessex : _bJohn Wiley, _c2011.
300			_axxiv, 642p.
500			_aIncludes biblographical references and Index 637-642p.
520			_aStatistical pattern recognition relates to the use of statistical techniques for analysing data measurements in order to extract information and make justified decisions. It is a very active area of study and research, which has seen many advances in recent years. Applications such as data mining, web searching, multimedia data retrieval, face recognition, and cursive handwriting recognition, all require robust and efficient pattern recognition techniques.This third edition provides an introduction to statistical pattern theory and techniques, with material drawn from a wide range of fields, including the areas of engineering, statistics, computer science and the social sciences. The book has been updated to cover new methods and applications, and includes a wide range of techniques such as Bayesian methods, neural networks, support vector machines, feature selection and feature reduction techniques.Technical descriptions and motivations are provided, and the techniques are illustrated using real examples.Statistical Pattern Recognition, 3rd Edition:Provides a self contained introduction to statistical pattern recognition.Includes new material presenting the analysis of complex networks. Introduces readers to methods for Bayesian density estimation.Presents descriptions of new applications in biometrics, security, finance and condition monitoring.Provides descriptions and guidance for implementing techniques, which will be invaluable to software engineers and developers seeking to develop real applications Describes mathematically the range of statistical pattern recognition techniques.Presents a variety of exercises including more extensive computer projects.The in-depth technical descriptions make the book suitable for senior undergraduate and graduate students in statistics, computer science and engineering. Statistical Pattern Recognition is also an excellent reference source for technical professionals. Chapters have been arranged to facilitate implementation of the techniques by software engineers and developers in non-statistical engineering fields.
650			_aComputer science.
650			_aStatistical pattern recognition. _9815862
650			_aStatistics.
700			_aCopsey, Keith D. _eco-author
942			_hB28930bD65,8(B) Q1 TB _cTEXL _2CC _n0
999			_c16754 _d16754