| Description |
1 online resource (631 pages) |
| Contents |
A Probabilistic Theory of Pattern Recognition; Editor's page; A Probabilistic Theory of Pattern Recognition; Copyright; Preface; Contents; 1 Introduction; 2 The Bayes Error; 3 Inequalities and Alternate Distance Measures; 4 Linear Discrimination; 5 Nearest Neighbor Rules; 6 Consistency; 7 Slow Rates of Convergence; 8 Error Estimation; 9 The Regular Histogram Rule; 10 Kernel Rules; 11 Consistency of the k-Nearest Neighbor Rule; 12 Vapnik -Chervonenkis Theory; 13 Combinatorial Aspects of Vapnik -Chervonenkis Theory; 14 Lower Bounds for Empirical Classifier Selection |
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15 The Maximum Likelihood Principle16 Parametric Classification; 17 Generalized Linear Discrimination; 18 Complexity Regularization; 19 Condensed and Edited Nearest Neighbor Rules; 20 Tree Classifiers; 21 Data- Dependent Partitioning; 22 Splitting the Data; 23 The Resubstitution Estimate; 24 Deleted Estimates of the Error Probability; 25 Automatic Kernel Rules; 26 Automatic Nearest Neighbor Rules; 27 Hypercubes and Discrete Spaces; 28 Epsilon Entropy and Totally Bounded Sets; 29 Uniform Laws of Large Numbers; 30 Neural Networks; 31 Other Error Estimates; 32 Feature Extraction; Appendix |
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NotationReferences; Author Index; Subject Index |
| Notes |
Print version record |
| Subject |
Pattern perception.
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Probabilities.
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Probability
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probability.
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Pattern perception.
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Probabilities.
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| Form |
Electronic book
|
| Author |
Karatzas, I
|
|
Yor, M
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| ISBN |
9781461207115 |
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1461207118 |
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