Pattern Recognition and Neural Networks
This 1996 book is a reliable account of the statistical framework for pattern recognition and machine learning. With unparalleled coverage and a wealth of case-studies this book gives valuable insight into both the theory and the enormously diverse applications (which can be found in remote sensing, astrophysics, engineering and medicine, for example). So that readers can develop their skills and understanding, many of the real data sets used in the book are available from the author's website: www.stats.ox.ac.uk/~ripley/PRbook/. For the same reason, many examples are included to illustrate real problems in pattern recognition. Unifying principles are highlighted, and the author gives an overview of the state of the subject, making the book valuable to experienced researchers in statistics, machine learning/artificial intelligence and engineering. The clear writing style means that the book is also a superb introduction for non-specialists.
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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