| Format: | Contents: Introduction.- The Bayes Error.- Inequalities and Alternate Distance Measures.- Linear Discrimination.- Nearest Neighbor Rules.- Consistency.- Slow Rates of Convergence.- Error Estimation.- The Regular Histogram Rule. - Kernel Rules.- Consistency of the K-Nearest Neighbor Rule.- Vapnik-Chervonenkis Theory. - Combinatorial Aspects of Vapnik-Chervonenkis Theory.- Lower Bounds for Empirical Classifier Selection.- The Maximum Likelihood Principle.- Parametric Classification.- Generalized Linear Discrimination. - Complexity Regularization.- Condensed and Edited Nearest Neighbor Rules.- Tree Classifiers.- Data-Dependent Partitioning. - Splitting the Data.- The Resubstitution Estimate.- Deleted Estimates of the Error Probability.- Automatic Kernel Rules.- Automatic Nearest Neighbor Rules.- Hypercubes and Discrete Spaces.- Epsilon Entropy and Totally Bounded Sets.- Uniform Laws of Large Numbers.- Neural Networks.- Other Error Estimates. |
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| Copyright: | 1996 |
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| ISBN: | 0387946187 |
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| LCCN: | Q327.D5 |
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| Publisher: | New York : Springer-Verlag, |
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| Physical Details: | XV, 636 p. : ill. ; 24 cm. |
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| Record No.: | 10527 |
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