Statistical pattern recognition /

• Κύριος συγγραφέας: Webb, A. R. (Andrew R.)
• Μορφή: Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: West Sussex, England ; New Jersey : Wiley, �2002.
• Έκδοση: 2nd ed.
• Θέματα: Pattern perception > Statistical methods.; Perception des structures > M�ethodes statistiques.; COMPUTERS > Optical Data Processing.; Discriminantanalyse.; Clusteranalyse.; Classificatie.; Reconhecimento de padr�oes.; Computa�c�ao gr�afica.; Computational and graphical statistics.; Electronic books.

 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. * 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. 

 1. Introduction to statistical pattern recognition -- Statistical pattern recognition -- Stages in a pattern recognition problem -- Issues -- Supervised versus unsupervised -- Approaches to statistical pattern recognition -- Multiple regression -- Outline of book -- 2. Density estimation -- parametric -- Normal-based models -- Normal mixture models -- Bayesian estimates -- 3. Density estimation -- nonparametric -- Histogram method -- k-nearest-neighbour method -- Expansion by basis functions -- Kernel methods -- 4. Linear discriminant analysis -- Two-class algorithms -- Multiclass algorithms -- Logistic discrimination -- 5. Nonlinear discriminant analysis -- kernel methods -- Optimisation criteria -- Radial basis functions -- Nonlinear support vector machines -- 6. Nonlinear discriminant analysis -- projection methods -- The multilayer perceptron -- Projection pursuit -- 7. Tree-based methods -- Classification trees -- Multivariate adaptive regression splines -- 8. Performance -- Performance assessment -- Comparing classifier performance -- Combining classifiers -- 9. Feature selection and extraction -- Feature selection -- Linear feature extraction -- Multidimensional scaling -- 10. Clustering -- Hierarchical methods -- Quick partitions -- Mixture models -- Sum-of-squares methods -- Cluster validity -- 11. Additional topics -- Model selection -- Learning with unreliable classification -- Missing data -- Outlier detection and robust procedures -- Mixed continuous and discrete variables -- Structural risk minimisation and the Vapnik-Chervonenkis dimension -- A. Measures of dissimilarity -- B. Parameter estimation -- C. Linear algebra -- D. Data -- E. Probability theory.