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Informationen zum Autor Dr Andrew Robert Webb , Senior Researcher, QinetiQ Ltd, Malvern, UK. Dr Keith Derek Copsey , Senior Researcher, QinetiQ Ltd, Malvern, UK. Klappentext Statistical pattern recognition relates to the use of statistical techniques for analysing data measurements in order to extract information and make justified decisions. It is a very active area of study and research, which has seen many advances in recent years. Applications such as data mining, web searching, multimedia data retrieval, face recognition, and cursive handwriting recognition, all require robust and efficient pattern recognition techniques.This third edition 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 has been updated to cover new methods and applications, and includes a wide range of techniques such as Bayesian methods, neural networks, support vector machines, feature selection and feature reduction techniques.Technical descriptions and motivations are provided, and the techniques are illustrated using real examples.Statistical Pattern Recognition, 3rd Edition:* Provides a self-contained introduction to statistical pattern recognition.* Includes new material presenting the analysis of complex networks.* Introduces readers to methods for Bayesian density estimation.* Presents descriptions of new applications in biometrics, security, finance and condition monitoring.* Provides descriptions and guidance for implementing techniques, which will be invaluable to software engineers and developers seeking to develop real applications* Describes mathematically the range of statistical pattern recognition techniques.* Presents a variety of exercises including more extensive computer projects.The in-depth technical descriptions make the book suitable for senior undergraduate and graduate students in statistics, computer science and engineering. Statistical Pattern Recognition is also an excellent reference source for technical professionals. Chapters have been arranged to facilitate implementation of the techniques by software engineers and developers in non-statistical engineering fields.www.wiley.com/go/statistical_pattern_recognition Zusammenfassung Statistical pattern recognition relates to the use of statistical techniques for analysing data measurements in order to extract information and make justified decisions. It is a very active area of study and research, which has seen many advances in recent years. Inhaltsverzeichnis Preface xix Notation xxiii 1 Introduction to Statistical Pattern Recognition 1 1.1 Statistical Pattern Recognition 1 1.1.1 Introduction 1 1.1.2 The Basic Model 2 1.2 Stages in a Pattern Recognition Problem 4 1.3 Issues 6 1.4 Approaches to Statistical Pattern Recognition 7 1.5 Elementary Decision Theory 8 1.5.1 Bayes' Decision Rule for Minimum Error 8 1.5.2 Bayes' Decision Rule for Minimum Error - Reject Option 12 1.5.3 Bayes' Decision Rule for Minimum Risk 13 1.5.4 Bayes' Decision Rule for Minimum Risk - Reject Option 15 1.5.5 Neyman-Pearson Decision Rule 15 1.5.6 Minimax Criterion 18 1.5.7 Discussion 19 1.6 Discriminant Functions 20 1.6.1 Introduction 20 1.6.2 Linear Discriminant Functions 21 1.6.3 Piecewise Linear Discriminant Functions 23 1.6.4 Generalised Linear Discriminant Function 24 1.6.5 Summary 26 1.7 Multiple Regression 27 1.8 Outline of Book 29 1.9 Notes and References 29 Exercises 31 2 Density Estimation - Parametric 33 2.1 Introduction 33 2.2 Estimating the Parameters of the Distributions 34 2.2.1 Estimative Approach 34
