LEADER 05468nam 2200697Ia 450 001 996212456103316 005 20230607221059.0 010 $a0-470-33906-3 010 $a0-470-85477-4 010 $a9786610270101 010 $a1-280-27010-1 010 $a0-470-85478-2 035 $a(CKB)1000000000356169 035 $a(EBL)158121 035 $a(OCoLC)53865202 035 $a(SSID)ssj0000251118 035 $a(PQKBManifestationID)11191515 035 $a(PQKBTitleCode)TC0000251118 035 $a(PQKBWorkID)10247589 035 $a(PQKB)11437128 035 $a(SSID)ssj0000366211 035 $a(PQKBManifestationID)12088486 035 $a(PQKBTitleCode)TC0000366211 035 $a(PQKBWorkID)10417776 035 $a(PQKB)11673966 035 $a(MiAaPQ)EBC158121 035 $a(EXLCZ)991000000000356169 100 $a20020529d2002 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 205 $a2nd ed. 210 $aWest Sussex, England ;$aNew Jersey $cWiley$dc2002 215 $a1 online resource (516 p.) 300 $aDescription based upon print version of record. 311 $a0-470-84513-9 311 $a0-470-84514-7 320 $aIncludes bibliographical references (p. [459]-490) 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 Supervised versus unsupervised; 1.5 Approaches to statistical pattern recognition; 1.5.1 Elementary decision theory; 1.5.2 Discriminant functions; 1.6 Multiple regression; 1.7 Outline of book; 1.8 Notes and references; Exercises; 2 Density estimation - parametric; 2.1 Introduction; 2.2 Normal-based models 327 $a2.2.1 Linear and quadratic discriminant functions2.2.2 Regularised discriminant analysis; 2.2.3 Example application study; 2.2.4 Further developments; 2.2.5 Summary; 2.3 Normal mixture models; 2.3.1 Maximum likelihood estimation via EM; 2.3.2 Mixture models for discrimination; 2.3.3 How many components?; 2.3.4 Example application study; 2.3.5 Further developments; 2.3.6 Summary; 2.4 Bayesian estimates; 2.4.1 Bayesian learning methods; 2.4.2 Markov chain Monte Carlo; 2.4.3 Bayesian approaches to discrimination; 2.4.4 Example application study; 2.4.5 Further developments; 2.4.6 Summary 327 $a2.5 Application studies2.6 Summary and discussion; 2.7 Recommendations; 2.8 Notes and references; Exercises; 3 Density estimation - nonparametric; 3.1 Introduction; 3.2 Histogram method; 3.2.1 Data-adaptive histograms; 3.2.2 Independence assumption; 3.2.3 Lancaster models; 3.2.4 Maximum weight dependence trees; 3.2.5 Bayesian networks; 3.2.6 Example application study; 3.2.7 Further developments; 3.2.8 Summary; 3.3 k-nearest-neighbour method; 3.3.1 k-nearest-neighbour decision rule; 3.3.2 Properties of the nearest-neighbour rule; 3.3.3 Algorithms; 3.3.4 Editing techniques 327 $a3.3.5 Choice of distance metric3.3.6 Example application study; 3.3.7 Further developments; 3.3.8 Summary; 3.4 Expansion by basis functions; 3.5 Kernel methods; 3.5.1 Choice of smoothing parameter; 3.5.2 Choice of kernel; 3.5.3 Example application study; 3.5.4 Further developments; 3.5.5 Summary; 3.6 Application studies; 3.7 Summary and discussion; 3.8 Recommendations; 3.9 Notes and references; Exercises; 4 Linear discriminant analysis; 4.1 Introduction; 4.2 Two-class algorithms; 4.2.1 General ideas; 4.2.2 Perceptron criterion; 4.2.3 Fisher's criterion 327 $a4.2.4 Least mean squared error procedures4.2.5 Support vector machines; 4.2.6 Example application study; 4.2.7 Further developments; 4.2.8 Summary; 4.3 Multiclass algorithms; 4.3.1 General ideas; 4.3.2 Error-correction procedure; 4.3.3 Fisher's criterion - linear discriminant analysis; 4.3.4 Least mean squared error procedures; 4.3.5 Optimal scaling; 4.3.6 Regularisation; 4.3.7 Multiclass support vector machines; 4.3.8 Example application study; 4.3.9 Further developments; 4.3.10 Summary; 4.4 Logistic discrimination; 4.4.1 Two-group case; 4.4.2 Maximum likelihood estimation 327 $a4.4.3 Multiclass logistic discrimination 330 $aStatistical pattern recognition is a very active area of study and research, which has seen many advances in recent years. New and emerging applications - such as data mining, web searching, multimedia data retrieval, face recognition, and cursive handwriting recognition - require robust and efficient pattern recognition techniques. Statistical decision making and estimation are regarded as fundamental to the study of pattern recognition. Statistical Pattern Recognition, Second Edition has been fully updated with new methods, applications and references. It provides a comprehensive intro 606 $aPattern perception$xStatistical methods 606 $aMathematical statistics 615 0$aPattern perception$xStatistical methods. 615 0$aMathematical statistics. 676 $a006.4 700 $aWebb$b Andrew R$g(Andrew Roy)$0268570 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996212456103316 996 $aStatistical pattern recognition$9678262 997 $aUNISA