Probabilistic Theory of Pattern Recognition.

Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Devroye, Luc
Otros Autores: Karatzas, I., Yor, M.
Formato: Electrónico eBook
Idioma:Inglés
Publicado: New York : Springer New York, 1996.
Edición:N.
Temas:
Pattern perception.
Probabilities.
Probability
Perception des structures.
Probabilités.
probability.
Pattern perception
Probabilities
Acceso en línea:Texto completo
Tabla de Contenidos:
  • 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.
  • 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.
  • NotationReferences; Author Index; Subject Index.

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