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Title:
The Nature of Statistical Learning Theory | SpringerLink
Description:
The aim of this book is to discuss the fundamental ideas which lie behind the statistical theory of learning and generalization. It considers learning from the general point of view of function estimation based on empirical data. Omitting proofs and technical details, the author concentrates on discussing the main results of learning theory and their connections to fundamental problems in statistics. These include: - the general setting of learning problems and the general model of minimizing the risk functional from empirical data - a comprehensive analysis of the empirical risk minimization principle and shows how this allows for the construction of necessary and sufficient conditions for consistency - non-asymptotic bounds for the risk achieved using the empirical risk minimization principle - principles for controlling the generalization ability of learning machines using small sample sizes - introducing a new type of universal learning machine that controls the generalization ability.
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Keywords {π}
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Topics {βοΈ}
statistical theory general model learning theory function estimation based risk functional universal learning machine generalizing deep learning learning problem vladimir learning processes vladimir construction convergence device instant download privacy choices/manage cookies interesting book helps learning problems function controlling considers learning learning machines efficient learning empirical modeling realm usa authors european economic area statistics general setting pdf read social media general point physics-based problems conditions privacy policy accepting optional cookies journal finder publish learning view book log general vapnik doihttps privacy policy empirical data empirical data books a personal data check access risk achieved top access access licence optional cookies information manage preferences author concentrates
Questions {β}
- Conclusion: What is Important in Learning Theory?
Schema {πΊοΈ}
Book:
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genre:
Mathematics and Statistics
Mathematics and Statistics (R0)
publisher:
name:Springer New York
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author:
name:Vladimir N. Vapnik
affiliation:
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address:
name:AT&T Bell Laboratories, Holmdel, USA
type:PostalAddress
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keywords:Statistica, algorithms, boundary element method, construction, controlling, convergence, function, functional, learning, learning algorithm, learning theory, model, proof, statistical theory, statistics
description:The aim of this book is to discuss the fundamental ideas which lie behind the statistical theory of learning and generalization. It considers learning from the general point of view of function estima
copyrightYear:1995
name:The Nature of Statistical Learning Theory
numberOfPages:15
isbn:978-1-4757-2440-0
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