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The Nature of Statistical Learning Theory
, 1999
"... 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 deve ..."
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Cited by 13236 (32 self)
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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
Introduction to Statistical Learning Theory
 In , O. Bousquet, U.v. Luxburg, and G. Rsch (Editors
, 2004
"... ..."
An Evolutionary Statistical Learning Theory Abstract—Statistical learning theory was developed by
"... Vapnik. It is a learning theory based on VapnikChervonenkis dimension. It also has been used in learning models as good analytical tools. In general, a learning theory has had several problems. Some of them are local optima and overfitting problems. As well, statistical learning theory has same pr ..."
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Vapnik. It is a learning theory based on VapnikChervonenkis dimension. It also has been used in learning models as good analytical tools. In general, a learning theory has had several problems. Some of them are local optima and overfitting problems. As well, statistical learning theory has same
TEEMS OF STATISTICAL LEARNING THEORY;!..! by
, 1957
"... This stpdy represents an extension of statistical learning theory to a class of twoperson, zerosum game sitpations. Becapse the theory has been mainly developed in connection with experiments dealing with individpal learning problems, its predictive success in an experimental area involving intera ..."
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This stpdy represents an extension of statistical learning theory to a class of twoperson, zerosum game sitpations. Becapse the theory has been mainly developed in connection with experiments dealing with individpal learning problems, its predictive success in an experimental area involving
Statistical Learning Theory for Location Fingerprinting in Wireless LANs
, 2002
"... In this paper, techniques and algorithms developed in the framework of statistical learning theory are analyzed and applied to the problem of determining the location of a wireless device by measuring the signal strengths from a set of access points (location fingerprinting). Statistical Learning Th ..."
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Cited by 95 (4 self)
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In this paper, techniques and algorithms developed in the framework of statistical learning theory are analyzed and applied to the problem of determining the location of a wireless device by measuring the signal strengths from a set of access points (location fingerprinting). Statistical Learning
Statistical learning theory: A primer
 International Journal of Computer Vision
, 2000
"... Abstract. In this paper we first overview the main concepts of Statistical Learning Theory, a framework in which learning from examples can be studied in a principled way. We then briefly discuss well known as well emerging learning techniques such as Regularization Networks and Support Vector Machi ..."
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Cited by 9 (1 self)
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Abstract. In this paper we first overview the main concepts of Statistical Learning Theory, a framework in which learning from examples can be studied in a principled way. We then briefly discuss well known as well emerging learning techniques such as Regularization Networks and Support Vector
Statistical Learning Theory
 MIT Encyclopedia of the Cognitive Sciences
, 1998
"... mples of loss functions. The choice of loss function depends on the nature of the modeling problem. From the point of view of utilitytheory, ff is a decision variable, z is an outcome, and Q(z# ff) is the negativeutility of the outcome given the decision. If the statistical properties of the da ..."
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Cited by 4 (3 self)
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mples of loss functions. The choice of loss function depends on the nature of the modeling problem. From the point of view of utilitytheory, ff is a decision variable, z is an outcome, and Q(z# ff) is the negativeutility of the outcome given the decision. If the statistical properties
Lectures on Statistical Learning Theory
, 2000
"... art. From model selection to adaptive estimation. In E. Torgersen D. Pollard and G. Yang, editors, Festschrift for Lucien Le Cam: Research papers in Probability and Statistics, pages 5587. Springer, New York, 1997. [78] L. Birg and P. Massart. Minimum contrast estimators on sieves: exponential bound ..."
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bounds and rates of convergence. Bernoulli, 4:329375, 1998. [79] Y. Freund. Self bounding learning algorithms. Proceedings of the Eleventh Annual Conference on Computational Learning Theory, pages 247258, 1998. [80] A.R. Gallant. Nonlinear Statistical Models. John Wiley, New York, 1987. [81] S. Geman
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