Journal Article10.1007/S001800050034
Support vector machine learning algorithm and transduction
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TL;DR: A recently developed method to transform the original input vectors into high-dimensional space, and then construct a linear regression function or hyperplane in that space by applying the kernel technique is reviewed.
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Abstract: The paper first reviews a recently developed method called the Support Vector Machine. The main feature of the method is to transform the original input vectors into high-dimensional space, and then construct a linear regression function or hyperplane in that space. The transformation is usually done by applying the kernel technique. The paper then shows that the same kernel technique can be applied to classical algorithms such as Ridge Regression. In conclusion, we present a new transductive learning algorithm that also allows us to compute confidence levels.
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•Proceedings Article
Ridge Regression Learning Algorithm in Dual Variables
Craig Saunders,Alexander Gammerman,Volodya Vovk +2 more
- 24 Jul 1998
TL;DR: A regression estimation algorithm which is a combination of the dual version of Ridge Regression is applied to the ANOVA enhancement of the infinitenode splines and the use of kernel functions, as used in Support Vector methods is introduced.
Improper Priors, Spline Smoothing and the Problem of Guarding Against Model Errors in Regression
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•Proceedings Article
Learning by transduction
Alexander Gammerman,Volodya Vovk,Vladimir Vapnik +2 more
- 24 Jul 1998
TL;DR: In this paper, a method for predicting a classification of an object given classifications of the objects in the training set, assuming that the pairs object/classification are generated by an i.i.d. process from a continuous probability distribution.
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