Pattern classification: a unified view of statistical and neural approaches
a unified view of statistical and neural approaches
The product of years of research and practical experience in pattern classification, this book offers a theory-based engineering perspective on neural networks and statistical pattern classification. Pattern Classification sheds new light on the relationship between seemingly unrelated approaches to pattern recognition, including statistical methods, polynomial regression, multilayer perceptron, and radial basis functions. Important topics such as feature selection, reject criteria, classifier performance measurement, and classifier combinations are fully covered, as well as material on techniques that, until now, would have required an extensive literature search to locate. A full program of illustrations, graphs, and examples helps make the operations and general properties of different classification approaches intuitively understandable.
Offering a lucid presentation of complex applications and their algorithms, Pattern Classification is an invaluable resource for researchers, engineers, and graduate students in this rapidly developing field.
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Statistical Decision Theory
Classification Based on Statistical Models Determined
11 other sections not shown
application approach artificial neuron backpropagation Chapter class label class membership class regions class-specific distributions class-specific probabilities classifier design cluster coefficient matrix components computed concept confidence mapping corresponding covariance matrix criterion decision space derived discriminant function d(v discriminant vector dk(y eigenvectors error rate Euclidean Euclidean distance example of 4.35 Figure functional approximation given histograms input vector large number learning rule learning sample learning set least mean-square linear combination mathematical mean measurement space measurement vector minimum multilayer perceptron neural network neuron node nonlinear normal distribution observation optimization optimum output parameters pattern classification system pattern classification task pattern source pixel point spread function polynomial classifier polynomial structure posteriori probabilities priori probabilities prob(fclv prob(v problem properties quadratic radial basis functions recognition recursive learning reject residual variance resulting sample set Section sigmoid function statistical model statistical moments stochastic process subsets target vector technique test set threshold two-dimensional V-space values weights