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Une théorie probabiliste de la reconnaissance de motifs Devroye Gyorfi Lugosi comme neuf HC-
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- Achat immédiatUne théorie probabiliste de la reconnaissance de motifs Devroye Gyorfi Lugosi comme neuf HC
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Numéro de l'objet eBay :115910532063
Dernière mise à jour le 17 mars 2024 23:44:08 Paris. Afficher toutes les modificationsAfficher toutes les modifications
Caractéristiques de l'objet
- État
- Book Title
- A Probabilistic Theory of Pattern Recognition
- ISBN
- 9780387946184
- Subject Area
- Mathematics, Computers, Psychology
- Publication Name
- Probabilistic Theory of Pattern Recognition
- Publisher
- Springer New York
- Item Length
- 9.3 in
- Subject
- Probability & Statistics / General, Cognitive Psychology & Cognition, Computer Vision & Pattern Recognition
- Publication Year
- 1996
- Series
- Stochastic Modelling and Applied Probability Ser.
- Type
- Textbook
- Format
- Hardcover
- Language
- English
- Item Height
- 0.6 in
- Item Weight
- 85.4 Oz
- Item Width
- 6.1 in
- Number of Pages
- Xv, 638 Pages
À propos de ce produit
Product Identifiers
Publisher
Springer New York
ISBN-10
0387946187
ISBN-13
9780387946184
eBay Product ID (ePID)
203797
Product Key Features
Number of Pages
Xv, 638 Pages
Publication Name
Probabilistic Theory of Pattern Recognition
Language
English
Publication Year
1996
Subject
Probability & Statistics / General, Cognitive Psychology & Cognition, Computer Vision & Pattern Recognition
Type
Textbook
Subject Area
Mathematics, Computers, Psychology
Series
Stochastic Modelling and Applied Probability Ser.
Format
Hardcover
Dimensions
Item Height
0.6 in
Item Weight
85.4 Oz
Item Length
9.3 in
Item Width
6.1 in
Additional Product Features
Intended Audience
Scholarly & Professional
LCCN
95-044633
Dewey Edition
20
TitleLeading
A
Series Volume Number
31
Number of Volumes
1 vol.
Illustrated
Yes
Dewey Decimal
003/.52/015192
Table Of Content
Preface * 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 * Feature extraction * Appendix * Notation * References * Index
Synopsis
A self-contained and coherent account of probabilistic techniques, covering: distance measures, kernel rules, nearest neighbour rules, Vapnik-Chervonenkis theory, parametric classification, and feature extraction. Each chapter concludes with problems and exercises to further the readers understanding. Both research workers and graduate students will benefit from this wide-ranging and up-to-date account of a fast- moving field., Pattern recognition presents a significant challege for scientists and engineers, and many different approaches have been proposed. This book provides a self-contained account of probabilistic techniques that have been applied to the subject. Researchers and graduate students will benefit from this wide-ranging account of the field., 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.
LC Classification Number
QA273.A1-274.9
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Évaluations en tant que vendeur (1 357)
- e***l (203)- Évaluations laissées par l'acheteur.6 derniers moisAchat vérifiéExcellent seller! Great transaction! I ordered this book in brand new, shrink/wrapped condition, and received it the next day with free shipping! I compared prices beforehand, and it was priced just $1 more than Amazon, and I would’ve had to pay shipping w/amazon since I’m not a Prime member. Plus, rarely does Amazon ship this fast anymore. This ebay listing was also cheaper than/similar to the others on ebay. Couldn’t be happier! This book was also packaged very well in a box. Thank you!!
- y***s (26)- Évaluations laissées par l'acheteur.6 derniers moisAchat vérifiéDVD received as described. Packing/shipping was very quick. Highly recommend this seller!
- 1***4 (353)- Évaluations laissées par l'acheteur.6 derniers moisAchat vérifiéGreat transaction! Item exactly as described, very well packaged for cross-country shipment, arrived in perfect condition. Would definitely buy from this seller again!The Guarneri Quartet Plays Mozart 6 CD Set 2014 RCA Red Seal Brand New Sealed (n°116152836255)
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