In this paper we study a dual version of the Ridge Regression procedure. It allows us to perform non-linear regression by construct-ing a linear regression function in a high di-mensional feature space. The feature space representation can result in a large increase in the number of parameters used by the al-gorithm. In order to combat this “curse of dimensionality”, the algorithm allows the use of kernel functions, as used in Support Vector methods. We also discuss a powerful family of kernel functions which is constructed using the ANOVA decomposition method from the kernel corresponding to splines with an infi-nite number of nodes. This paper introduces a regression estimation algorithm which is a combination of these two elements: the dual version of Ridge Regression is applied to the ANOVA enhancement of the infinite-node splines. Experimental results are then presented (based on the Boston Housing data set) which indicate the performance of this algorithm relative to other algorithms. 1
This paper collects some ideas targeted at advancing our understanding of the feature spaces associated with support vector (SV) kernel functions. We first discuss the geometry ...
Traditional classification approaches generalize poorly on image classification tasks, because of the high dimensionality of the feature space. This paper shows that support vec...
A non-linear classification technique based on Fisher's discriminant is proposed. Main ingredient is the kernel trick which allows to efficiently compute the linear Fisher discr...
Introduction to support vector learning roadmap. Part 1 Theory: three remarks on the support vector method of function estimation, Vladimir Vapnik generalization performance of ...
Projection pursuit regression and kernel regression are methods for estimating a smooth function of several variables from noisy data obtained at scattered sites. Methods based ...