A Probabilistic Theory of Pattern RecognitionSpringer Science & Business Media, 27.11.2013 - 638 sivua Pattern recognition presents one of the most significant challenges for scientists and engineers, and many different approaches have been proposed. The aim of this book is to provide a self-contained account of probabilistic analysis of these approaches. The book includes a discussion of distance measures, nonparametric methods based on kernels or nearest neighbors, Vapnik-Chervonenkis theory, epsilon entropy, parametric classification, error estimation, free classifiers, and neural networks. Wherever possible, distribution-free properties and inequalities are derived. A substantial portion of the results or the analysis is new. Over 430 problems and exercises complement the material. |
Sisältö
| 1 | |
| 21 | |
Linear Discrimination | 39 |
Problems and Exercises | 56 |
Weighted Nearest Neighbor Rules | 71 |
Problems and Exercises | 83 |
Consistency | 91 |
Problems and Exercises | 107 |
Condensed and Edited Nearest Neighbor Rules 303 | 302 |
Tree Classifiers | 315 |
DataDependent Partitioning | 363 |
Splitting the Data 387 | 386 |
The Resubstitution Estimate | 397 |
Deleted Estimates of the Error Probability | 407 |
Automatic Kernel Rules 423 | 422 |
Automatic Nearest Neighbor Rules | 451 |
Problems and Exercises | 118 |
The Regular Histogram Rule | 133 |
Problems and Exercises | 142 |
Consistency of the kNearest Neighbor Rule 169 | 168 |
VapnikChervonenkis Theory | 187 |
Combinatorial Aspects of VapnikChervonenkis Theory 215 | 214 |
Lower Bounds for Empirical Classifier Selection | 233 |
The Maximum Likelihood Principle | 249 |
Parametric Classification | 263 |
Generalized Linear Discrimination | 279 |
Complexity Regularization | 289 |
Hypercubes and Discrete Spaces 461 | 460 |
Epsilon Entropy and Totally Bounded Sets | 479 |
Uniform Laws of Large Numbers 489 | 488 |
Neural Networks | 507 |
Other Error Estimates | 549 |
Feature Extraction 561 | 560 |
Appendix | 575 |
Notation | 591 |
| 619 | |
| 627 | |
Muita painoksia - Näytä kaikki
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |
A Probabilistic Theory of Pattern Recognition Luc Devroye,Laszlo Gyorfi,Gábor Lugosi Esikatselu ei käytettävissä - 2014 |
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Esikatselu ei käytettävissä - 2013 |
Yleiset termit ja lausekkeet
algorithm Assume asymptotic Bayes error binary Cauchy-Schwarz inequality cells Chapter class of classifiers classification rule condition converges to zero Corollary data points decision defined deleted estimate denotes density Devroye distribution empirical error error estimate error probability example finite fixed function gn(x HINT histogram rule Hoeffding's inequality hyperplane hyperrectangles inequality integer k-d tree k-nearest k-NN rule kernel rule Lemma linear classifier maximum likelihood method minimizing the empirical monotone nearest neighbor rule neural network node obtained otherwise pairs parameters partition probability measure probability of error proof of Theorem Prove random variables rate of convergence rectangles risk minimization rule based rule gn sample selected shatter coefficients Show sigmoid ſº split squared error structural risk minimization subsets supremum tree classifier upper bound values VC dimension vector
Kirjaluettelon tiedot
| Otsikko | A Probabilistic Theory of Pattern Recognition Nide 31 / Stochastic Modelling and Applied Probability |
| Kirjailijat | Luc Devroye, Laszlo Györfi, Gabor Lugosi |
| Painos | kuvitettu |
| Kustantaja | Springer Science & Business Media, 2013 |
| ISBN | 1461207118, 9781461207115 |
| Pituus | 638 sivua |
| Vie sitaatti | BiBTeX EndNote RefMan |