Andrew Webb

Statistical Pattern Recognition

• Gebundenes Buch

Produktbeschreibung

This book 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. It provides a valuable link between the application areas - such as database design, artificial neural networks, and decision support - and the more diverse theoretical topics available to the practitioner or researcher. Each section concludes with a description of the wide range of practical applications that have been addressed and the further developments of theoretical techniques.

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Statistical 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 makingand 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 introduction to this vibrant area - with material drawn from engineering, statistics, computer science and the social sciences - and covers many application areas, such as database design, artificial neural networks, and decision support systems. Provides a self-contained introduction to statistical pattern recognition. Each technique described is illustrated by real examples. Covers Bayesian methods, neural networks, support vector machines, and unsupervised classification. Each section concludes with a description of the applications that have been addressed and with further developments of the theory. Includes background material on dissimilarity, parameter estimation, data, linear algebra and probability. Features a variety of exercises, from 'open-book' questions to more lengthy projects.The book is aimed primarily at senior undergraduate and graduate students studying statistical pattern recognition, pattern processing, neural networks, and data mining, in both statistics and engineering departments. It is also an excellent source of reference for technical professionals working in advanced information development environments.

Produktdetails

• Verlag: Wiley & Sons
• 2nd ed.
• Seitenzahl: 514
• Englisch
• Abmessung: 250mm
• Gewicht: 1012g
• ISBN-13: 9780470845134
• ISBN-10: 0470845139
• Artikelnr.: 14924556

Autorenporträt

Andrew Webb, PhD, is a faculty member in the Department of Electrical and Computer Engineering and the Beckman Institute for Advanced Science and Technology at the University of Illinois at Urbana-Champaign. Dr. Webb has contributed to many areas of magnetic resonance imaging including developments in radiofrequency coil design, feedback control of thermal processes, techniques for localized spectroscopy, and functional brain mapping. He was awarded a Whitaker Foundation Research Award and a National Science Foundation Career Award in 1997, a Wolfgang-Paul Prize from the Alexander von Humbolt Foundation in 2001, and Xerox and Willett awards for young faculty in 2002. He is a Senior Member of the IEEE.

Inhaltsangabe

Preface.
Notation.
1 Introduction to statistical pattern recognition.
1.1 Statistical pattern recognition.
1.2 Stages in a pattern recognition problem.
1.3 Issues.
1.4 Supervised versus unsupervised.
1.5 Approaches to statistical pattern recognition.
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.
2.3 Normal mixture models.
2.4 Bayesian estimates.
2.5 Application studies.
2.6 Summary and discussion.
2.7 Recommendations.
2.8 Notes and references.
Exercises.
3 Density estimation - nonparametric.
3.1 Introduction.
3.2 Histogram method.
3.3 k-nearest-neighbour method.
3.4 Expansion by basis functions.
3.5 Kernel methods.
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.
4.3 Multiclass algorithms.
4.4 Logistic discrimination.
4.5 Application studies.
4.6 Summary and discussion.
4.7 Recommendations.
4.8 Notes and references.
Exercises.
5 Nonlinear discriminant analysis - kernel methods.
5.1 Introduction.
5.2 Optimisation criteria.
5.3 Radial basis functions.
5.4 Nonlinear support vector machines.
5.5 Application studies.
5.6 Summary and discussion.
5.7 Recommendations.
5.8 Notes and references.
Exercises.
6 Nonlinear discriminant analysis - projection methods.
6.1 Introduction.
6.2 The multilayer perceptron.
6.3 Projection pursuit.
6.4 Application studies.
6.5 Summary and discussion.
6.6 Recommendations.
6.7 Notes and references.
Exercises.
7 Tree-based methods.
7.1 Introduction.
7.2 Classification trees.
7.3 Multivariate adaptive regression splines.
7.4 Application studies.
7.5 Summary and discussion.
7.6 Recommendations.
7.7 Notes and references.
Exercises.
8 Performance.
8.1 Introduction.
8.2 Performance assessment.
8.3 Comparing classifier performance.
8.4 Combining classifiers.
8.5 Application studies.
8.6 Summary and discussion.
8.7 Recommendations.
8.8 Notes and references.
Exercises.
9 Feature selection and extraction.
9.1 Introduction.
9.2 Feature selection.
9.3 Linear feature extraction.
9.4 Multidimensional scaling.
9.5 Application studies.
9.6 Summary and discussion.
9.7 Recommendations.
9.8 Notes and references.
Exercises.
10 Clustering.
10.1 Introduction.
10.2 Hierarchical methods.
10.3 Quick partitions.
10.4 Mixture models.
10.5 Sum-of-squares methods.
10.6 Cluster validity.
10.7 Application studies.
10.8 Summary and discussion.
10.9 Recommendations.
10.10 Notes and references.
Exercises.
11 Additional topics.
11.1 Model selection.
11.2 Learning with unreliable classification.
11.3 Missing data.
11.4 Outlier detection and robust procedures.
11.5 Mixed continuous and discrete variables.
11.6 Structural risk minimisation and the Vapnik-Chervonenkis dimension.
A Measures of dissimilarity.
A.1 Measures of dissimilarity.
A.2 Distances between distributions.
A.3 Discussion.
B Parameter estimation.
B.1 Parameter estimation.
C Linear algebra.
C.1 Basic properties and definitions.
C.2 Notes and references.
D Data.
D.1 Introduction.
D.2 Formulating the problem.
D.3 Data collection.
D.4 Initial examination of data.
D.5 Data sets.
D.6 Notes and references.
E Probability theory.
E.1 Definitions and terminology.
E.2 Normal distribution.
E.3 Probability distributions.
References.
Index.