LEADER 05624nam 22006974a 450 001 9910877862403321 005 20200520144314.0 010 $a1-280-26895-6 010 $a9786610268955 010 $a0-470-09015-4 010 $a1-60119-496-X 010 $a0-470-09014-6 035 $a(CKB)1000000000356580 035 $a(EBL)232696 035 $a(SSID)ssj0000071581 035 $a(PQKBManifestationID)11107242 035 $a(PQKBTitleCode)TC0000071581 035 $a(PQKBWorkID)10091241 035 $a(PQKB)10032024 035 $a(MiAaPQ)EBC232696 035 $a(CaSebORM)9780470090138 035 $a(OCoLC)85820614 035 $a(PPN)115219536 035 $a(OCoLC)840430470 035 $a(OCoLC)ocn840430470 035 $a(EXLCZ)991000000000356580 100 $a20040517d2004 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aClassification, parameter estimation, and state estimation $ean engineering approach using MATLAB /$fF. van der Heijden ... [et al.] 205 $a1st edition 210 $aChichester, West Sussex, Eng. ;$aHoboken, NJ $cWiley$dc2004 215 $a1 online resource (441 p.) 300 $aDescription based upon print version of record. 311 $a0-470-09013-8 320 $aIncludes bibliographical references and index. 327 $aClassification, Parameter Estimation and State Estimation; Contents; Preface; Foreword; 1 Introduction; 1.1 The scope of the book; 1.1.1 Classification; 1.1.2 Parameter estimation; 1.1.3 State estimation; 1.1.4 Relations between the subjects; 1.2 Engineering; 1.3 The organization of the book; 1.4 References; 2 Detection and Classification; 2.1 Bayesian classification; 2.1.1 Uniform cost function and minimum error rate; 2.1.2 Normal distributed measurements; linear and quadratic classifiers; 2.2 Rejection; 2.2.1 Minimum error rate classification with reject option 327 $a2.3 Detection: the two-class case2.4 Selected bibliography; 2.5 Exercises; 3 Parameter Estimation; 3.1 Bayesian estimation; 3.1.1 MMSE estimation; 3.1.2 MAP estimation; 3.1.3 The Gaussian case with linear sensors; 3.1.4 Maximum likelihood estimation; 3.1.5 Unbiased linear MMSE estimation; 3.2 Performance of estimators; 3.2.1 Bias and covariance; 3.2.2 The error covariance of the unbiased linear MMSE estimator; 3.3 Data fitting; 3.3.1 Least squares fitting; 3.3.2 Fitting using a robust error norm; 3.3.3 Regression; 3.4 Overview of the family of estimators; 3.5 Selected bibliography 327 $a3.6 Exercises4 State Estimation; 4.1 A general framework for online estimation; 4.1.1 Models; 4.1.2 Optimal online estimation; 4.2 Continuous state variables; 4.2.1 Optimal online estimation in linear-Gaussian systems; 4.2.2 Suboptimal solutions for nonlinear systems; 4.2.3 Other filters for nonlinear systems; 4.3 Discrete state variables; 4.3.1 Hidden Markov models; 4.3.2 Online state estimation; 4.3.3 Offline state estimation; 4.4 Mixed states and the particle filter; 4.4.1 Importance sampling; 4.4.2 Resampling by selection; 4.4.3 The condensation algorithm; 4.5 Selected bibliography 327 $a4.6 Exercises5 Supervised Learning; 5.1 Training sets; 5.2 Parametric learning; 5.2.1 Gaussian distribution, mean unknown; 5.2.2 Gaussian distribution, covariance matrix unknown; 5.2.3 Gaussian distribution, mean and covariance matrix both unknown; 5.2.4 Estimation of the prior probabilities; 5.2.5 Binary measurements; 5.3 Nonparametric learning; 5.3.1 Parzen estimation and histogramming; 5.3.2 Nearest neighbour classification; 5.3.3 Linear discriminant functions; 5.3.4 The support vector classifier; 5.3.5 The feed-forward neural network; 5.4 Empirical evaluation; 5.5 References 327 $a5.6 Exercises6 Feature Extraction and Selection; 6.1 Criteria for selection and extraction; 6.1.1 Inter/intra class distance; 6.1.2 Chernoff-Bhattacharyya distance; 6.1.3 Other criteria; 6.2 Feature selection; 6.2.1 Branch-and-bound; 6.2.2 Suboptimal search; 6.2.3 Implementation issues; 6.3 Linear feature extraction; 6.3.1 Feature extraction based on the Bhattacharyya distance with Gaussian distributions; 6.3.2 Feature extraction based on inter/intra class distance; 6.4 References; 6.5 Exercises; 7 Unsupervised Learning; 7.1 Feature reduction; 7.1.1 Principal component analysis 327 $a7.1.2 Multi-dimensional scaling 330 $aClassification, Parameter Estimation and State Estimation is a practical guide for data analysts and designers of measurement systems and postgraduates students that are interested in advanced measurement systems using MATLAB. 'Prtools' is a powerful MATLAB toolbox for pattern recognition and is written and owned by one of the co-authors, B. Duin of the Delft University of Technology. After an introductory chapter, the book provides the theoretical construction for classification, estimation and state estimation. The book also deals with the skills required to bring the theoretical co 606 $aEngineering mathematics$xData processing 606 $aMeasurement$xData processing 606 $aEstimation theory$xData processing 615 0$aEngineering mathematics$xData processing. 615 0$aMeasurement$xData processing. 615 0$aEstimation theory$xData processing. 676 $a681/.2 701 $aHeijden$b Ferdinand van der$0951628 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910877862403321 996 $aClassification, parameter estimation, and state estimation$94064658 997 $aUNINA