The Experts below are selected from a list of 452283 Experts worldwide ranked by ideXlab platform
Vladimir Vapnik - One of the best experts on this subject based on the ideXlab platform.
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An overview of statistical Learning Theory
IEEE transactions on neural networks, 1999Co-Authors: Vladimir VapnikAbstract:Statistical Learning Theory was introduced in the late 1960's. Until the 1990's it was a purely theoretical analysis of the problem of function estimation from a given collection of data. In the middle of the 1990's new types of Learning algorithms (called support vector machines) based on the developed Theory were proposed. This made statistical Learning Theory not only a tool for the theoretical analysis but also a tool for creating practical algorithms for estimating multidimensional functions. This article presents a very general overview of statistical Learning Theory including both theoretical and algorithmic aspects of the Theory. The goal of this overview is to demonstrate how the abstract Learning Theory established conditions for generalization which are more general than those discussed in classical statistical paradigms and how the understanding of these conditions inspired new algorithmic approaches to function estimation problems.
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the nature of statistical Learning Theory
1995Co-Authors: Vladimir VapnikAbstract:Setting of the Learning problem consistency of Learning processes bounds on the rate of convergence of Learning processes controlling the generalization ability of Learning processes constructing Learning algorithms what is important in Learning Theory?.
Gabor Lugosi - One of the best experts on this subject based on the ideXlab platform.
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introduction to statistical Learning Theory
Lecture Notes in Computer Science, 2004Co-Authors: Olivier Bousquet, Stephane Boucheron, Gabor LugosiAbstract:The goal of statistical Learning Theory is to study, in a statistical framework, the properties of Learning algorithms. In particular, most results take the form of so-called error bounds. This tutorial introduces the techniques that are used to obtain such results.
Olivier Bousquet - One of the best experts on this subject based on the ideXlab platform.
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introduction to statistical Learning Theory
Lecture Notes in Computer Science, 2004Co-Authors: Olivier Bousquet, Stephane Boucheron, Gabor LugosiAbstract:The goal of statistical Learning Theory is to study, in a statistical framework, the properties of Learning algorithms. In particular, most results take the form of so-called error bounds. This tutorial introduces the techniques that are used to obtain such results.
Cherkasskyvladimir - One of the best experts on this subject based on the ideXlab platform.
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Motion Estimation Using Statistical Learning Theory
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004Co-Authors: Wechslerharry, Duriczoran, Lifayin, CherkasskyvladimirAbstract:Abstract--This paper describes a novel application of Statistical Learning Theory (SLT) to single motion estimation and tracking. The problem of motion estimation can be related to statistical mode...
Rajeeva L. Karandikar - One of the best experts on this subject based on the ideXlab platform.
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System identification: a Learning Theory approach
Proceedings of the 40th IEEE Conference on Decision and Control (Cat. No.01CH37228), 1Co-Authors: Mathukumalli Vidyasagar, Rajeeva L. KarandikarAbstract:The problem of system identification is formulated as a problem in statistical Learning Theory, because statistical Learning Theory is devoted to the derivation of finite time estimates. If system identification is to be combined with robust control Theory to develop a sound Theory of indirect adaptive control, it is essential to have finite time estimates of the sort provided by statistical Learning Theory. As an illustration of the approach, a result is derived showing that in the case of systems with fading memory, it is possible to combine standard result's in statistical Learning Theory (suitably modified to the present situation) with some fading memory arguments to obtain finite time estimates of the desired kind. In the case of linear systems, the results proved here are not overly conservative, but are more so in the case of nonlinear systems where the adjustable parameters enter linearly into the model description. Though the actual results derived here are rather preliminary in nature, it is hoped that future researchers will pursue the ideas presented here to extend the Theory further.
