regression analysis using core vector machine technique based on kernel function optimization

Core vector regression (cvr) is an extension of the core vector machine algorithm for regression estimation by generalizing the minimum bounding ball (meb) problem. as an estimator, both the kernel function and its parameters can significantly affect the prediction precision of cvr. in this paper, a method to improve cvr performance using pre-processing based on data feature extraction and grid algorithm is proposed to obtain appropriate parameters values of the main formulation and its kernel function. the cvr estimated mean absolute error rate here is the evaluation criterion of the proposed method that should be minimized. in addition, some benchmark datasets out of different databases were used to evaluate the proposed parameter optimization approach. the obtained numerical results show that the proposed method can reduce the cvr error with an acceptable time and space complexity. therefore, it is able to deal with very large data and real world regression problems.