Pattern Recognition and Neural Networks
Pattern recognition has long been studied in relation to many different (and mainly unrelated) applications, such as remote sensing, computer vision, space research, and medical imaging. In this book Professor Ripley brings together two crucial ideas in pattern recognition; statistical methods and machine learning via neural networks. Unifying principles are brought to the fore, and the author gives an overview of the state of the subject. Many examples are included to illustrate real problems in pattern recognition and how to overcome them.This is a self-contained account, ideal both as an introduction for non-specialists readers, and also as a handbook for the more expert reader.
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Introduction and Examples
Statistical Decision Theory
Linear Discriminant Analysis
Feedforward Neural Networks
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algorithm analysis approach approximation asymptotic average Bayes risk Bayes rule Bayesian binary bound Breiman Chapter choose class densities classifier clique clusters compute conditional independence consider convergence covariance matrix cross-validation Cushing's syndrome dataset density estimation deviance dimensions dissimilarity error rate example Figure Gibbs sampler given gives hidden layer hidden units idea inputs iterative kernel linear combination linear discriminant log-likelihood logistic Machine Learning Mahalanobis distance marginal Markov Markov property maximize maximum likelihood measure methods minimize mixture moral graph multivariate neighbour neural networks node non-linear optimal outliers output units parameters pattern recognition perceptron plug-in points posterior probabilities predictive principal components prior problem procedure projection pursuit Proposition pruning quadratic quadratic rule random variables regression Ripley sample Section shows smoothing splines split Statistical subset Suppose test set training set update values variance VC dimension vertex vertices weight decay zero