Statistical Pattern Recognition - Info and Reading Options

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Methods. 10.5.1 Clustering criteria. 10.5.2 Clustering algorithms. 10.5.3 Vector quantisation. 10.5.4 Example application study. 10.5.5 Further developments. 10.5.6 Summary. 10.6 Cluster validity. 10.6.1 Introduction. 10.6.2 Distortion measures. 10.6.3 Choosing the number of clusters. 10.6.4 Identifying genuine clusters. 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. 11.1.1 Separate training and test sets. 11.1.2 Cross-validation. 11.1.3 The Bayesian viewpoint. 11.1.4 Akaike's information criterion. 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. 11.6.1 Bounds on the expected risk. 11.6.2 The Vapnik-Chervonenkis dimension. A Measures of dissimilarity. A.1 Measures of dissimilarity. A.1.1 Numeric variables. A.1.2 Nominal and ordinal variables. A.1.3 Binary variables. A.1.4 Summary. A.2 Distances between distributions. A.2.1 Methods based on prototype vectors. A.2.2 Methods based on probabilistic distance. A.2.3 Probabilistic dependence. A.3 Discussion. B Parameter estimation. B.1 Parameter estimation. B.1.1 Properties of estimators. B.1.2 Maximum likelihood. B.1.3 Problems with maximum likelihood. B.1.4 Bayesian estimates. 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.

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