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. |
Kirjan sisältä
Tulokset 1 - 5 kokonaismäärästä 81
Sivu x
... Probability of Error 05888 61 61 63 66 69 5.5 The Asymptotic Error Probability of Weighted Nearest Neighbor Rules 71 5.6 k - Nearest Neighbor Rules : Even k 74 5.7 Inequalities for the Probability of Error 75 5.8 5.9 6.1 Behavior When L ...
... Probability of Error 05888 61 61 63 66 69 5.5 The Asymptotic Error Probability of Weighted Nearest Neighbor Rules 71 5.6 k - Nearest Neighbor Rules : Even k 74 5.7 Inequalities for the Probability of Error 75 5.8 5.9 6.1 Behavior When L ...
Sivu 8
... 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 ...
... 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 ...
Sivu 10
... probability . η Any function g : Rd → { 0 , 1 } defines a classifier or a decision function . The error probability of g is L ( g ) = P { g ( X ) 7 Y } . Of particular interest is the Bayes decision function g * ( x ) = { 1 if n ( x ) ...
... probability . η Any function g : Rd → { 0 , 1 } defines a classifier or a decision function . The error probability of g is L ( g ) = P { g ( X ) 7 Y } . Of particular interest is the Bayes decision function g * ( x ) = { 1 if n ( x ) ...
Sivu 16
... probability in L1 - sense , then the error probability of decision g is near the optimal decision g * . Theorem 2.2 . For the error probability of the plug - in decision g defined above , we have P { g ( X ) Y } L * = 2 — · In ( x ) -1 ...
... probability in L1 - sense , then the error probability of decision g is near the optimal decision g * . Theorem 2.2 . For the error probability of the plug - in decision g defined above , we have P { g ( X ) Y } L * = 2 — · In ( x ) -1 ...
Sivu 17
Luc Devroye, Laszlo Györfi, Gabor Lugosi. Theorem 2.3 . The error probability of the decision defined above is bounded from above by P { g ( X ) Y ) L * < - Lo - - | ( 1 − n ( x ) ) — ño ( x ) \ μ ( dx ) + ↓ 2 In ( x ) — ñ1 ( x ) \ μ ...
Luc Devroye, Laszlo Györfi, Gabor Lugosi. Theorem 2.3 . The error probability of the decision defined above is bounded from above by P { g ( X ) Y ) L * < - Lo - - | ( 1 − n ( x ) ) — ño ( x ) \ μ ( dx ) + ↓ 2 In ( x ) — ñ1 ( x ) \ μ ...
Sisältö
1 | |
4 | |
21 | |
27 | |
54 | |
Nearest Neighbor Rules | 60 |
4 | 67 |
6 | 74 |
Parametric Classification | 263 |
Generalized Linear Discrimination | 279 |
Complexity Regularization | 289 |
Condensed and Edited Nearest Neighbor Rules 303 | 302 |
Tree Classifiers | 315 |
DataDependent Partitioning | 363 |
Splitting the Data 387 | 386 |
The Resubstitution Estimate | 397 |
11 | 81 |
2 | 92 |
6 | 100 |
8 | 106 |
2 | 113 |
Error Estimation | 120 |
The Regular Histogram Rule | 133 |
Kernel Rules | 153 |
Consistency of the kNearest Neighbor Rule | 168 |
VapnikChervonenkis Theory | 187 |
Combinatorial Aspects of VapnikChervonenkis Theory | 214 |
4 | 224 |
1 | 234 |
The Maximum Likelihood Principle | 249 |
Deleted Estimates of the Error Probability | 407 |
Automatic Kernel Rules 423 | 422 |
Automatic Nearest Neighbor Rules | 451 |
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 |
Author Index | 619 |
Subject Index | 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
a₁ algorithm Assume asymptotic b₁ 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 HINT histogram rule Hoeffding's inequality hyperplane hyperrectangles inequality integer Jensen's inequality k-d tree k-nearest k-NN rule kernel rule L(gn Lemma linear classifier maximum likelihood minimizing the empirical nearest neighbor rule neural network node obtained otherwise pairs parameters partition pattern recognition probability of error proof of Theorem Prove random variables rate of convergence rectangles risk minimization rule gn sample selected shatter coefficients Show sigmoid split squared error structural risk minimization subsets tree classifiers universally consistent upper bound values vc dimension vector X₁ Y₁ фес