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...

We propose a novel idea of regression – balancing the distances from a regression function to all examples. We created a method, called balanced support vector regression (balanced SVR) in which we incorporated this idea to support vector regression (SVR) by adding an equality constraint to the SVR optimization problem. We implemented our method for two versions of SVR: ε-insensitive support ve...

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...

Reservoir permeability is a critical parameter for characterization of the hydrocarbon reservoirs. In fact, determination of permeability is a crucial task in reserve estimation, production and development. Traditional methods for permeability prediction are well log and core data analysis which are very expensive and time-consuming. Well log data is an alternative approach for prediction of pe...

Porosity is considered as an important petrophysical parameter in characterizing reservoirs, calculating in-situ oil reserves, and production evaluation. Nowadays, using intelligent techniques has become a popular method for porosity estimation. Support vector machine (SVM) a new intelligent method with a great generalization potential of modeling non-linear relationships has been introduced fo...

reservoir permeability is a critical parameter for characterization of the hydrocarbon reservoirs. in fact, determination of permeability is a crucial task in reserve estimation, production and development. traditional methods for permeability prediction are well log and core data analysis which are very expensive and time-consuming. well log data is an alternative approach for prediction of pe...

porosity is considered as an important petrophysical parameter in characterizing reservoirs, calculating in-situ oil reserves, and production evaluation. nowadays, using intelligent techniques has become a popular method for porosity estimation. support vector machine (svm) a new intelligent method with a great generalization potential of modeling non-linear relationships has been introduced fo...

Recent work shows that Support vector machines (SVMs) can be solved efficiently in the primal. This paper follows this line of research and shows how to build sparse support vector regression (SVR) in the primal, thus providing for us scalable, sparse support vector regression algorithm, named SSVR-SRS. Empirical comparisons show that the number of basis functions required by the proposed algor...