An introduction to pattern recognition and machine learning / / Paul Fieguth

• Record Nr.: UNINA9910629297203321
• Autore: Fieguth Paul
• Pubbl/distr/stampa: Cham, Switzerland : , : Springer, , [2022]; ©2022
• ISBN: 9783030959951; 9783030959937
• Descrizione fisica: 1 online resource (481 pages)
• Disciplina: 006.31
• Soggetti: Machine learning; Pattern perception; Aprenentatge automàtic; Llibres electrònics
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Intro -- 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.; 8.2 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.; B.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.