Statistical Pattern Recognition

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.