support vector regression (svr) solves regression problems based on the concept of support vector machine (svm). in this paper, a new model of svr with probabilistic constraints is proposed that any of output data and bias are considered the random variables with uniform probability functions. using the new proposed method, the optimal hyperplane regression can be obtained by solving a quadrati...

In this paper, we first introduce the notion of $c$-affine functions for $c> 0$. Then we deal with some properties of strongly convex functions in real inner product spaces by using a quadratic support function at each point which is $c$-affine. Moreover, a Hyers–-Ulam stability result for strongly convex functions is shown.

in this paper, we first introduce the notion of $c$-affine functions for $c> 0$.then we deal with some properties of strongly convex functions in real inner product spaces by using a quadratic support function at each point which is $c$-affine. moreover, a hyers–-ulam stability result for strongly convex functions is shown.

there are many numerous methods for solving large-scale problems in which some of them are very flexible and efficient in both linear and non-linear cases. league championship algorithm is such algorithm which may be used in the mentioned problems. in the current paper, a new play-off approach will be adapted on league championship algorithm for solving large-scale problems. the proposed algori...

Support vector machines (SVMs) are an extremely successful class of classification and regression algorithms. Building an SVM entails the solution of a constrained convex quadratic programming problem which is quadratic in the number of training samples. Previous parallel implementations of SVM solvers sequentially solved subsets of the complete problem, which is problematic when the solution r...

Copula has become a standard tool in describing dependent relations between random variables. This paper proposes a nonparametric bivariate copula estimation method based on shape-restricted -support vector regression ( -SVR). This method explicitly supplements the classical -SVR with constraints related to three shape restrictions: grounded, marginal and 2-increasing, which are the necessary a...

The Kernel-Adatron (KA) algorithm was recently introduced as an alternative to quadratic programming for training support vector machines. In this paper we investigate three variants of the original KA algorithm and demonstrate through examples that they deliver excellent accuracy. The examples also show that one of these variants has rather different learning properties to the other two.

Support Vector Regression (SVR) solves regression problems based on the concept of Support Vector Machine (SVM). In this paper, a new model of SVR with probabilistic constraints is proposed that any of output data and bias are considered the random variables with uniform probability functions. Using the new proposed method, the optimal hyperplane regression can be obtained by solving a quadrati...