LEADER 05739nam 2200493 450 001 9910629297203321 005 20230508112217.0 010 $a9783030959951$b(electronic bk.) 010 $z9783030959937 035 $a(MiAaPQ)EBC7134132 035 $a(Au-PeEL)EBL7134132 035 $a(CKB)25299477300041 035 $a(PPN)266353738 035 $a(EXLCZ)9925299477300041 100 $a20230321d2022 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 13$aAn introduction to pattern recognition and machine learning /$fPaul Fieguth 210 1$aCham, Switzerland :$cSpringer,$d[2022] 210 4$dŠ2022 215 $a1 online resource (481 pages) 311 08$aPrint version: Fieguth, Paul An Introduction to Pattern Recognition and Machine Learning Cham : Springer International Publishing AG,c2022 9783030959937 320 $aIncludes bibliographical references and index. 327 $aIntro -- Preface -- Table of Contents -- List of Examples -- List of Algorithms -- Notation -- 1 Overview -- 2 Introduction to Pattern Recognition -- 2.1 What Is Pattern Recognition? -- 2.2 Measured Patterns -- 2.3 Classes -- 2.4 Classification -- 2.5 Types of Classification Problems -- Case Study 2: Biometrics -- Numerical Lab 2: The Iris Dataset -- Further Reading -- Sample Problems -- References -- 3 Learning -- Case Study 3: The Netflix Prize -- Numerical Lab 3: Overfitting and Underfitting -- Summary -- Further Reading -- Sample Problems -- References -- 4 Representing Patterns -- 4.1 Similarity -- 4.2 Class Shape -- 4.3 Cluster Synthesis -- Case Study 4: Defect Detection -- Numerical Lab 4: Working with Random Numbers -- Further Reading -- Sample Problems -- References -- 5 Feature Extraction and Selection -- 5.1 Fundamentals of Feature Extraction -- 5.2 Feature Extraction and Selection -- Case Study 5: Image Searching -- Numerical Lab 5: Extracting Features and Plotting Classes -- Further Reading -- Sample Problems -- References -- 6 Distance-Based Classification -- 6.1 Definitions of Distance -- 6.2 Class Prototype -- 6.3 Distance-Based Classification -- 6.4 Classifier Variations -- Case Study 6: Hand-writing Recognition -- Numerical Lab 6: Distance-Based Classifiers -- Further Reading -- Sample Problems -- References -- 7 Inferring Class Models -- 7.1 Parametric Estimation -- 7.2 Parametric Model Learning -- 7.3 Nonparametric Model Learning -- 7.3.1 Histogram Estimation -- 7.3.2 Kernel-Based Estimation -- 7.3.3 Neighbourhood-based Estimation -- 7.4 Distribution Assessment -- Case Study 7: Object Recognition -- Numerical Lab 7: Parametric and Nonparametric Estimation -- Further Reading -- Sample Problems -- References -- 8 Statistics-Based Classification -- 8.1 Non-Bayesian Classification: Maximum Likelihood. 327 $a8.2 Bayesian Classification: Maximum a Posteriori -- 8.3 Statistical Classification for Normal Distributions -- 8.4 Classification Error -- 8.5 Other Statistical Classifiers -- Case Study 8: Medical Assessments -- Numerical Lab 8: Statistical and Distance-Based Classifiers -- Further Reading -- Sample Problems -- References -- 9 Classifier Testing and Validation -- 9.1 Working with Data -- 9.2 Classifier Evaluation -- 9.3 Classifier Validation -- Case Study 9: Autonomous Vehicles -- Numerical Lab 9: Leave-One-Out Validation -- Further Reading -- Sample Problems -- References -- 10 Discriminant-Based Classification -- 10.1 Linear Discriminants -- 10.2 Discriminant Model Learning -- 10.3 Nonlinear Discriminants -- 10.4 Multi-Class Problems -- Case Study 10: Digital Communications -- Numerical Lab 10: Discriminants -- Further Reading -- Sample Problems -- References -- 11 Ensemble Classification -- 11.1 Combining Classifiers -- 11.2 Resampling Strategies -- 11.3 Sequential Strategies -- 11.4 Nonlinear Strategies -- 11.4.1 Neural Network Learning -- 11.4.2 Deep Neural Network Classifiers -- Case Study 11: Interpretability and Ethics of Large Networks -- Numerical Lab 11: Ensemble Classifiers -- Further Reading -- Sample Problems -- References -- 12 Model-Free Classification -- 12.1 Unsupervised Learning -- 12.1.1 K-Means Clustering -- 12.1.2 Kernel K-Means Clustering -- 12.1.3 Mean-Shift Clustering -- 12.1.4 Hierarchical Clustering -- 12.2 Network-Based Clustering -- 12.3 Semi-Supervised Learning -- Case Study 12: Ancient Text Analysis: Who Wrote What? -- Numerical Lab 12: Clustering -- Further Reading -- Sample Problems -- References -- 13 Conclusions and Directions -- Appendices -- A Algebra Review -- Further Reading -- Sample Problems -- References -- B Random Variables and Random Vectors -- B.1 Random Variables -- B.2 Expectations. 327 $aB.3 Conditional Statistics -- B.4 Random Vectors and Covariances -- B.5 Outliers and Heavy-Tail Distributions -- B.6 Sample Statistics -- Further Reading -- Sample Problems -- References -- C Introduction to Optimization -- C.1 Basic Principles -- C.2 One-Dimensional Optimization -- C.3 Multi-Dimensional Optimization -- C.4 Multi-Objective Optimization -- Further Reading -- Sample Problems -- References -- D Mathematical Derivations -- Index. 606 $aMachine learning 606 $aPattern perception 606 $aAprenentatge automātic$2thub 608 $aLlibres electrōnics$2thub 615 0$aMachine learning. 615 0$aPattern perception. 615 7$aAprenentatge automātic 676 $a006.31 700 $aFieguth$b Paul$0818349 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 912 $a9910629297203321 996 $aAn Introduction to Pattern Recognition and Machine Learning$92968762 997 $aUNINA