This paper studies the addition of linear constraints to Support Vector Regression when kernel is linear. Adding those into problem allows add prior knowledge on estimator obtained, such as finding positive vector, probability vector or monotone data. We prove that related optimization stays a semi-definite quadratic problem. also propose generalization Sequential Minimal Optimization algorithm...

Support Vector Regression (SVR) is usually pursued using the 2–insensitive loss function while, alternatively, the initial regression problem can be reduced to a properly defined classification one. In either case, slack variables have to be introduced in practical interesting problems, the usual choice being the consideration of linear penalties for them. In this work we shall discuss the solu...

Support Vector Regression (SVR) has been a long standing problem in machine learning, and gains its popularity on various computer vision tasks. In this paper, we propose a structured support vector regression framework by extending the max-margin principle to incorporate spatial correlations among neighboring pixels. The objective function in our framework considers both label information and ...

Support Vector Regression (SVR), a category for Support Vector Machine (SVM) attempts to minimize the generalization error bound so as to achieve generalized performance. Regression is that of finding a function which approximates mapping from an input domain to the real numbers on the basis of a training sample. Support vector regression is the natural extension of large margin kernel methods ...

Wepropose the reduced twin support vector regressor (RTSVR) that uses the notion of rectangular kernels to obtain significant improvements in execution time over the twin support vector regressor (TSVR), thus facilitating its application to larger sized datasets. & 2011 Elsevier B.V. All rights reserved.