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The Nature of Statistical Learning Theory
other
Author(s):
Vladimir N. Vapnik
Publication date
(Print):
2000
Publisher:
Springer New York
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Related collections
Numerical Algebra, Matrix Theory, Differential-Algebraic Equations, and Control Theory
Author and book information
Book
ISBN (Print):
978-1-4419-3160-3
ISBN (Electronic):
978-1-4757-3264-1
Publication date (Print):
2000
DOI:
10.1007/978-1-4757-3264-1
SO-VID:
36666731-5c0d-4987-a0b7-b46ddfc02cc6
License:
http://www.springer.com/tdm
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Book chapters
pp. 1
Introduction: Four Periods in the Research of the Learning Problem
pp. 17
Setting of the Learning Problem
pp. 35
Consistency of Learning Processes
pp. 69
Bounds on the Rate of Convergence of Learning Processes
pp. 93
Controlling the Generalization Ability of Learning Processes
pp. 123
Methods of Pattern Recognition
pp. 181
Methods of Function Estimation
pp. 225
Direct Methods in Statistical Learning Theory
pp. 267
The Vicinal Risk Minimization Principle and the SVMs
pp. 291
Conclusion: What Is Important in Learning Theory?
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