Background and purpose: Machine learning is a class of modern and strong tools that can solve many important problems that nowadays humans may be faced with. Support vector regression (SVR) is a way to build a regression model which is an incredible member of the machine learning family. SVR has been proven to be an effective tool in real-value function estimation. As a supervised-learning appr...

OBJECTIVES Searching for chemicals that will safely enhance transdermal drug delivery is a significant challenge. This study applies support vector regression (SVR) for the first time to estimating the optimal formulation design of transdermal hydrocortisone formulations. METHODS The aim of this study was to apply SVR methods with two different kernels in order to estimate the enhancement rat...

In this research, a robust optimization approach applied to support vector regression (SVR) is investigated. A novel kernel based-method is developed to address the problem of data uncertainty where each data point is inside a sphere. The model is called robust SVR. Computational results show that the resulting robust SVR model is better than traditional SVR in terms of robustness and generaliz...

Extreme Support Vector Machine (ESVM) is a nonlinear robust SVM algorithm based on regularized least squares optimization for binary-class classification. In this paper, a novel algorithm for regression tasks, Extreme Support Vector Regression (ESVR), is proposed based on ESVM. Moreover, kernel ESVR is suggested as well. Experiments show that, ESVR has a better generalization than some other tr...

Support Vector Regression (SVR) is Support Vector Machine (SVM) is used for regression case. Regression method is one of prediction season method has been commonly used. SVR process requires kernel functions to transform the non-linear inputs into a high dimensional feature space. This research was conducted to predict rainfall in the dry season at 15 weather stations in Indramayu district. The...

The purpose of present study is to investigate a nonparametric model that improves accuracy of option prices found by previous models. In this study option prices are calculated using multiple kernel Support Vector Regression with different norm values and their results are compared. L1norm multiple kernel learning Support Vector Regression (MKLSVR) has been successfully applied to option price...

In the recent years, generalizations of support vector methods for analyzing interval-valued data have been suggested in both the regression and classification contexts. Standard Support Vector methods for precise data formalize these statistical problems as optimization problems that can be based on various loss functions. In the case of Support Vector Regression (SVR), on which we focus here,...

In this paper, we incorporate the concept of fuzzy set theory into the support vector regression (SVR). In our proposed method, target outputs of training samples are considered to be fuzzy numbers and then, membership function of actual output (objective hyperplane in high dimensional feature space) is obtained. Two main properties of our proposed method are: (1) membership function of actual ...

Support Vector Regression (SVR) is a kernel based regression method capable of implementing a variety of regularisation techniques. Implementation of SVR usually follows a dual optimisation technique which includes Vapnik's -insensitive zone. The number of terms in the resulting SVR approximation function is dependent on the size of this zone, but improving sparsity by increasing the size of th...