Support vector machine (SVM) has been very successful in pattern recognition and function estimation problems for crisp data. This paper proposes a new method to evaluate interval linear and nonlinear regression models combining the possibility and necessity estimation formulation with the principle of SVM. For data sets with crisp inputs and interval outputs, the possibility and necessity mode...

Twin support vector regression (TSVR) is a new regression algorithm, which aims at finding -insensitive upand down-bound functions for the training points. In order to do so, one needs to resolve a pair of smaller-sized quadratic programming problems (QPPs) rather than a single large one in a classical SVR. However, the same penalties are given to the samples in TSVR. In fact, samples in the di...

In this article, we propose a novel regression method which is based solely on Support Vector Classification. The experiments show that the new method has comparable or better generalization performance than ε-insensitive Support Vector Regression. The tests were performed on synthetic data, on various publicly available regression data sets, and on stock price data. Furthermore, we demonstrate...

In this tutorial we give an overview of the basic ideas underlying Support Vector (SV) machines for function estimation. Furthermore, we include a summary of currently used algorithms for training SV machines, covering both the quadratic (or convex) programming part and advanced methods for dealing with large datasets. Finally, we mention some modifications and extensions that have been applied...

Kernel support vector (SV) regression has successfully been used for prediction of nonlinear and complicated data. However, like other kernel methods such as support vector machine (SVM) classification, the quality of SV regression depends on proper choice of kernel functions and their parameters. Kernel selection for model selection is conventionally performed through repeated cross validation...

In this paper, we introduce a new method: support vector regression (SVR) method to modeling low temperature co-fired ceramic (LTCC) multilayer interconnect. SVR bases on structural risk minimization (SRM) principle, which leads to good generalization ability. A LTCC based stripline-to-stripline interconnect used as example to verify the proposed method. Experiment results show that the develop...

Abstract In recent years, renewable energies have been covering an increasing part of the worldwide electrical power demand. The additional volatility introduced to power grids by weather dependent renewable energy sources, i.e., wind and solar, makes it necessary to improve the accuracy of energy forecasts, so that the underlying electrical grid can be operated in a cost efficient way. Governm...

The ν-Support Vector Regression (ν-SVR) is an effective regression learning algorithm, which has the advantage of using a parameter ν on controlling the number of support vectors and adjusting the width of the tube automatically. However, compared to ν-Support Vector Classification (ν-SVC) (Schölkopf et al., 2000), ν-SVR introduces an additional linear term into its objective function. Thus, di...

The problem of multiclass classification is considered and resolved through the multiresponse linear regression approach. Scores are used to encode the class labels into multivariate responses. The regression of scores on input attributes is used to extract a lowdimensional linear discriminant subspace. The classification training and prediction are carried out in this low-dimensional subspace....

Batch implementations of support vector regression (SVR) are inefficient when used in an on-line setting because they must be retrained from scratch every time the training set is modified. Following an incremental support vector classification algorithm introduced by Cauwenberghs and Poggio (2001), we have developed an accurate on-line support vector regression (AOSVR) that efficiently updates...