Open AccessProceedings Article
Semiparametric Support Vector and Linear Programming Machines
Alexander J. Smola,Thilo-Thomas Frieß,Bernhard Schölkopf +2 more
- 01 Dec 1998
- Vol. 11, pp 585-591
TL;DR: Two learning algorithms are extended - Support Vector machines and Linear Programming machines - to this case and experimental results for SV machines are given.
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Abstract: Semiparametric models are useful tools in the case where domain knowledge exists about the function to be estimated or emphasis is put onto understandability of the model We extend two learning algorithms - Support Vector machines and Linear Programming machines to this case and give experimental results for SV machines
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A tutorial on support vector regression
TL;DR: This tutorial gives an overview of the basic ideas underlying Support Vector (SV) machines for function estimation, and includes a summary of currently used algorithms for training SV machines, covering both the quadratic programming part and advanced methods for dealing with large datasets.
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- 16 Jul 2001
TL;DR: The result shows that a wide range of problems have optimal solutions that live in the finite dimensional span of the training examples mapped into feature space, thus enabling us to carry out kernel algorithms independent of the (potentially infinite) dimensionality of the feature space.
•Proceedings Article
The Kernel Trick for Distances
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- 01 Jan 2000
TL;DR: A method is described which, like the kernel trick in support vector machines (SVMs), lets us generalize distance-based algorithms to operate in feature spaces, usually nonlinearly related to the input space.
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TL;DR: This paper surveys the progress in model learning with a strong focus on robot control on a kinematic as well as dynamical level and deduces future directions of real-time learning algorithms.
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Bernhard E. Boser,Isabelle Guyon,Vladimir Vapnik +2 more
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Efficient and Adaptive Estimation for Semiparametric Models
Peter J. Bickel
- 01 Sep 1993
TL;DR: Asymptotic Inference for (Finite-Dimensional) Parametric Models as mentioned in this paper has been studied in the context of infinite-dimensional parametric models, where information bounds for Euclidean parameters in infinite-dimensional models have been derived.
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