A course in density estimation
A course in density estimation
Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Analysis of Symbolic Data: Exploratory Methods for Extracting Statistical Information from Complex Data
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This article surveys a range of classification (discrimination) methods, which are based on probabilistic or statistical models such as Bayes methods, maximum likelihood, nearest neighbor classifiers, nonparametric kernel density methods, plug-in rules, and so on. Additionally, we point to various algorithmic approaches for classification such as neural networks, support-vector machines, and decision trees which are, however, fully discussed in subsequent sections of this handbook. A major part of this article is devoted to the specification and estimation of various types of recovery rates and misclassification probabilities of a (fixed or data dependent) classifier. Finally, we describe some preprocessing methods for the selection of most informative variables.