Statistical Pattern Recognition

Andrew R. Webb (Autor)

Buch | Hardcover

514 Seiten

2002 | 2nd Revised edition
John Wiley & Sons Ltd (Verlag)
978-0-470-84513-4 (ISBN)

Infos

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 making and 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. aeo Provides a self--contained introduction to statistical pattern recognition. aeo Each technique described is illustrated by real examples. aeo Covers Bayesian methods, neural networks, support vector machines, and unsupervised classification. aeo Each section concludes with a description of the applications that have been addressed and with further developments of the theory.
aeo Includes background material on dissimilarity, parameter estimation, data, linear algebra and probability. aeo Features a variety of exercises, from a open--booka 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. For further information on the techniques and applications discussed in this book please visit www.statistical--pattern--recognition.net

Inhalt

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.

Produkt-Details

• Erscheint lt. Verlag: 18.7.2002
• Zusatzinfo: Ill.
• Verlagsort: Chichester
• Sprache: englisch
• Maße: 179 x 255 mm
• Gewicht: 1036 g
• Einbandart: gebunden
• Themenwelt: Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
• Themenwelt: Mathematik / Informatik ► Mathematik ► Angewandte Mathematik
• Themenwelt: Mathematik / Informatik ► Mathematik ► Statistik
• Themenwelt: Mathematik / Informatik ► Mathematik ► Wahrscheinlichkeit / Kombinatorik
• ISBN-10: 0-470-84513-9 / 0470845139
• ISBN-13: 978-0-470-84513-4 / 9780470845134
• Zustand: Neuware