Pattern Classification: A Unified View of Statistical and Neural ApproachesPATTERN CLASSIFICATION 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. |
Contents
Statistical Decision Theory | 19 |
Fundamental Approaches | 31 |
Classification Based on Statistical Models Determined | 44 |
18 other sections not shown
Common terms and phrases
application approach artificial neuron backpropagation Chapter class label class membership class regions class-specific probabilities coefficient matrix components computed concept confidence mapping corresponding covariance matrix criterion decision space derived discriminant function d(v discriminant vector eigenvectors error rate estimating function Euclidean distance feature Figure functional approximation given histograms input vector K classes large number layer learning rule learning samples learning set least mean-square linear combination mathematical mean measurement space measurement vector minimum moment matrix multilayer perceptron neural network node nonlinear normal distribution optimization output parameters pattern classification system pattern classification task pattern source polynomial classifier polynomial structure posteriori probabilities principal-axis transform priori probabilities prob(k|v prob(v problem properties quadratic radial basis functions recognition reconstruction error recursive learning reject representing residual variance resulting sample set Section sigmoid function statistical model statistical moments stochastic process subset target vector technique test set threshold two-dimensional V-space values weights