The Resource Statistical learning theory, Vladimir N. Vapnik

Statistical learning theory, Vladimir N. Vapnik

Label
Statistical learning theory
Title
Statistical learning theory
Statement of responsibility
Vladimir N. Vapnik
Creator
Subject
Language
eng
Summary
A comprehensive look at learning and generalization theory. The statistical theory of learning and generalization concerns the problem of choosing desired functions on the basis of empirical data. Highly applicable to a variety of computer science and robotics fields, this book offers lucid coverage of the theory as a whole. Presenting a method for determining the necessary and sufficient conditions for consistency of learning process, the author covers function estimates from small data pools, applying these estimations to real-life problems, and much more
Member of
Action
committed to retain for EAST
Cataloging source
DLC
http://library.link/vocab/creatorName
Vapnik, Vladimir Naumovich
Illustrations
illustrations
Index
index present
LC call number
Q325.7
LC item number
.V38 1998
Literary form
non fiction
Nature of contents
bibliography
http://library.link/vocab/subjectName
  • Computational learning theory
  • convergence
  • traitement signal
  • régression
  • reconnaissance statistique forme
  • machine à vecteur support
  • perceptron
  • gestion risque
  • minimisation
  • mesure probabilistique
  • problème mal posé
  • inférence statistique
  • théorie apprentissage
  • Apprentissage informatique, Théorie de l'
  • Computational learning theory
  • Machine-learning
  • Leertheorieën
  • Théorie de l'apprentissage informatique
  • Théorie de l'apprentissage
  • Aspect stratégique
  • Théorie de l'estimation
  • Fonction vectorielle
  • Apprentissage automatique
  • Induction (logique)
  • Inférence
  • Lerntheorie
  • Mathematische Lerntheorie
  • Statistik
  • Calcul
  • Statistique
  • Acquisition de connaissances
Label
Statistical learning theory, Vladimir N. Vapnik
Instantiates
Publication
Note
"A Wiley-Interscience publication."
Bibliography note
Includes bibliographical references (p. 723-732) and index
Carrier category
volume
Carrier category code
nc
Carrier MARC source
rdacarrier
Content category
text
Content type code
txt
Content type MARC source
rdacontent
Contents
  • Estimation of the Probability Measure and Problem of Learning.
  • 3.
  • Conditions for Consistency of Empirical Risk Minimization Principle.
  • 4.
  • Bounds on the Risk for Indicator Loss Functions.
  • App. to Ch. 4.
  • Lower Bounds on the Risk of the ERM Principle.
  • 5.
  • Bounds on the Risk for Real-Valued Loss Functions.
  • 6.
  • Introduction: The Problem of Induction and Statistical Inference
  • The Structural Risk Minimization Principle.
  • App. to Ch. 6.
  • Estimating Functions on the Basis of Indirect Measurements.
  • 7.
  • Stochastic III-Posed Problems.
  • 8.
  • Estimating the Values of Function at Given Points
  • I.
  • Theory of Learning and Generalization.
  • 1.
  • Two Approaches to the Learning Problem.
  • App. to Ch. 1.
  • Methods for Solving III-Posed Problems.
  • 2.
Dimensions
25 cm.
Extent
xxiv, 736 pages
Isbn
9780471030034
Lccn
97037075
Media category
unmediated
Media MARC source
rdamedia
Media type code
n
Other physical details
illustrations
System control number
  • (OCoLC)37567297
  • (OCoLC)ocm37567297
Label
Statistical learning theory, Vladimir N. Vapnik
Publication
Note
"A Wiley-Interscience publication."
Bibliography note
Includes bibliographical references (p. 723-732) and index
Carrier category
volume
Carrier category code
nc
Carrier MARC source
rdacarrier
Content category
text
Content type code
txt
Content type MARC source
rdacontent
Contents
  • Estimation of the Probability Measure and Problem of Learning.
  • 3.
  • Conditions for Consistency of Empirical Risk Minimization Principle.
  • 4.
  • Bounds on the Risk for Indicator Loss Functions.
  • App. to Ch. 4.
  • Lower Bounds on the Risk of the ERM Principle.
  • 5.
  • Bounds on the Risk for Real-Valued Loss Functions.
  • 6.
  • Introduction: The Problem of Induction and Statistical Inference
  • The Structural Risk Minimization Principle.
  • App. to Ch. 6.
  • Estimating Functions on the Basis of Indirect Measurements.
  • 7.
  • Stochastic III-Posed Problems.
  • 8.
  • Estimating the Values of Function at Given Points
  • I.
  • Theory of Learning and Generalization.
  • 1.
  • Two Approaches to the Learning Problem.
  • App. to Ch. 1.
  • Methods for Solving III-Posed Problems.
  • 2.
Dimensions
25 cm.
Extent
xxiv, 736 pages
Isbn
9780471030034
Lccn
97037075
Media category
unmediated
Media MARC source
rdamedia
Media type code
n
Other physical details
illustrations
System control number
  • (OCoLC)37567297
  • (OCoLC)ocm37567297

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