This paper deals with transforming Support vector regression (SVR) models into fuzzy systems (FIS). It is highlighted that trained support vector based models can be used for the construction of fuzzy rule-based regression models. However, the transformed support vector model does not automatically result in an interpretable fuzzy model. Training of a support vector model results a complex rule...

We show that the Bayesian evidence framework can be applied to both-support vector regression (-SVR) and-support vector regression (-SVR) algorithms. Standard SVR training can be regarded as performing level one inference of the evidence framework, while levels two and three allow automatic adjustments of the regularization and kernel parameters respectively, without the need of a validation set.

Extreme Support Vector Machine (ESVM), a variant of ELM, is a nonlinear SVM algorithm based on regularized least squares optimization. In this chapter, a regression algorithm, Extreme Support Vector Regression (ESVR), is proposed based on ESVM. Experiments show that, ESVR has a better generalization ability than the traditional ELM.Furthermore, ESVMcan reach comparable accuracy as SVR and LS-SV...

A new regression technique based on Vapnik’s concept of support vectors is introduced. We compare support vector regression (SVR) with a committee regression technique (bagging) based on regression trees and ridge regression done in feature space. On the basis of these experiments, it is expected that SVR will have advantages in high dimensionality space because SVR optimization does not depend...

− Instead of minimizing the observed training error, Support Vector Regression (SVR) attempts to minimize the generalization error bound so as to achieve generalized performance. The idea of SVR is based on the computation of a linear regression function in a high dimensional feature space where the input data are mapped via a nonlinear function. SVR has been applied in various fields – time se...

The problem of tolerant data fitting by a nonlinear surface, induced by a kernel-based support vector machine [19], is formulated as a linear program with fewer number of variables than that of other linear programming formulations [17]. A generalization of the linear programming chunking algorithm [1] for arbitrary kernels [10] is implemented for solving problems with very large datasets where...

To segment a image with strongly varying object sizes results generally in under-segmentation of small structures or over-segmentation of big ones, which consequences poor classification accuracies. A strategy to produce multiple segmentations of one image and classification with support vector machines (SVM) of this segmentation stack afterwards is shown.

We rederive a form of Joachims’ ξα method for tuning Support Vector Machines by the same approach as was used to derive the GACV, and show how the two methods are related. We generalize the ξα method to the nonstandard case of nonrepresentative training set and unequal misclassification costs and compare the result to the GACV estimate for the standard and nonstandard cases.