LEADER 06189nam 2200697 a 450 001 9910139578003321 005 20200520144314.0 010 $a1-119-96140-8 010 $a1-283-28311-5 010 $a9786613283115 010 $a1-118-30535-3 010 $a1-119-95295-6 010 $a1-119-95296-4 035 $a(CKB)2550000000052719 035 $a(EBL)819173 035 $a(OCoLC)763160180 035 $a(SSID)ssj0000541559 035 $a(PQKBManifestationID)11351597 035 $a(PQKBTitleCode)TC0000541559 035 $a(PQKBWorkID)10499089 035 $a(PQKB)11435366 035 $a(MiAaPQ)EBC819173 035 $a(Au-PeEL)EBL819173 035 $a(CaPaEBR)ebr10500952 035 $a(CaONFJC)MIL328311 035 $a(PPN)243652593 035 $a(EXLCZ)992550000000052719 100 $a20110715d2011 uy 0 101 0 $aeng 135 $aur|n||||||||| 181 $ctxt 182 $cc 183 $acr 200 10$aStatistical pattern recognition$b[electronic resource] /$fAndrew R. Webb, Keith D. Copsey 205 $a3rd ed. 210 $aHoboken $cWiley$d2011 215 $a1 online resource (xxiv, 642 pages) $cillustrations, tables 300 $aDescription based upon print version of record. 311 $a0-470-68227-2 311 $a0-470-68228-0 320 $aIncludes bibliographical references and index. 327 $aStatistical Pattern Recognition; Contents; Preface; Notation; 1 Introduction to Statistical Pattern Recognition; 1.1 Statistical Pattern Recognition; 1.1.1 Introduction; 1.1.2 The Basic Model; 1.2 Stages in a Pattern Recognition Problem; 1.3 Issues; 1.4 Approaches to Statistical Pattern Recognition; 1.5 Elementary Decision Theory; 1.5.1 Bayes' Decision Rule for Minimum Error; 1.5.2 Bayes' Decision Rule for Minimum Error - Reject Option; 1.5.3 Bayes' Decision Rule for Minimum Risk; 1.5.4 Bayes' Decision Rule for Minimum Risk - Reject Option; 1.5.5 Neyman-Pearson Decision Rule 327 $a1.5.6 Minimax Criterion1.5.7 Discussion; 1.6 Discriminant Functions; 1.6.1 Introduction; 1.6.2 Linear Discriminant Functions; 1.6.3 Piecewise Linear Discriminant Functions; 1.6.4 Generalised Linear Discriminant Function; 1.6.5 Summary; 1.7 Multiple Regression; 1.8 Outline of Book; 1.9 Notes and References; Exercises; 2 Density Estimation - Parametric; 2.1 Introduction; 2.2 Estimating the Parameters of the Distributions; 2.2.1 Estimative Approach; 2.2.2 Predictive Approach; 2.3 The Gaussian Classifier; 2.3.1 Specification; 2.3.2 Derivation of the Gaussian Classifier Plug-In Estimates 327 $a2.3.3 Example Application Study2.4 Dealing with Singularities in the Gaussian Classifier; 2.4.1 Introduction; 2.4.2 Na ??ve Bayes; 2.4.3 Projection onto a Subspace; 2.4.4 Linear Discriminant Function; 2.4.5 Regularised Discriminant Analysis; 2.4.6 Example Application Study; 2.4.7 Further Developments; 2.4.8 Summary; 2.5 Finite Mixture Models; 2.5.1 Introduction; 2.5.2 Mixture Models for Discrimination; 2.5.3 Parameter Estimation for Normal Mixture Models; 2.5.4 Normal Mixture Model Covariance Matrix Constraints; 2.5.5 How Many Components?; 2.5.6 Maximum Likelihood Estimation via EM 327 $a2.5.7 Example Application Study2.5.8 Further Developments; 2.5.9 Summary; 2.6 Application Studies; 2.7 Summary and Discussion; 2.8 Recommendations; 2.9 Notes and References; Exercises; 3 Density Estimation - Bayesian; 3.1 Introduction; 3.1.1 Basics; 3.1.2 Recursive Calculation; 3.1.3 Proportionality; 3.2 Analytic Solutions; 3.2.1 Conjugate Priors; 3.2.2 Estimating the Mean of a Normal Distribution with Known Variance; 3.2.3 Estimating the Mean and the Covariance Matrix of a Multivariate Normal Distribution; 3.2.4 Unknown Prior Class Probabilities; 3.2.5 Summary; 3.3 Bayesian Sampling Schemes 327 $a3.3.1 Introduction3.3.2 Summarisation; 3.3.3 Sampling Version of the Bayesian Classifier; 3.3.4 Rejection Sampling; 3.3.5 Ratio of Uniforms; 3.3.6 Importance Sampling; 3.4 Markov Chain Monte Carlo Methods; 3.4.1 Introduction; 3.4.2 The Gibbs Sampler; 3.4.3 Metropolis-Hastings Algorithm; 3.4.4 Data Augmentation; 3.4.5 Reversible Jump Markov Chain Monte Carlo; 3.4.6 Slice Sampling; 3.4.7 MCMC Example - Estimation of Noisy Sinusoids; 3.4.8 Summary; 3.4.9 Notes and References; 3.5 Bayesian Approaches to Discrimination; 3.5.1 Labelled Training Data; 3.5.2 Unlabelled Training Data 327 $a3.6 Sequential Monte Carlo Samplers 330 $a"Statistical Pattern Recognition provides an introduction to statistical pattern theory and techniques, with material drawn from a wide range of fields, including the areas of engineering, statistics, computer science and the social sciences. The book describes techniques for analysing data comprising measurements made on individuals or objects. The techniques are used to make a prediction such as disease of a patient, the type of object illuminated by a radar, economic forecast. Emphasis is placed on techniques for classification, a term used for predicting the class or group an object belongs to (based on a set of exemplars) and for methods that seek to discover natural groupings in a data set. Each section concludes with a description of the wide range of practical applications that have been addressed and the further developments of theoretical techniques and includes a variety of exercises, from 'open-book' questions to more lengthy projects. New material is presented, including the analysis of complex networks and basic techniques for analysing the properties of datasets and also introduces readers to the use of variational methods for Bayesian density estimation and looks at new applications in biometrics and security"--$cProvided by publisher. 606 $aPattern perception$xStatistical methods 615 0$aPattern perception$xStatistical methods. 676 $a006.4 686 $aMAT029000$2bisacsh 700 $aWebb$b A. R$g(Andrew R.)$0915397 701 $aCopsey$b Keith D$0915398 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910139578003321 996 $aStatistical pattern recognition$92051917 997 $aUNINA