Balanced Support Vector Regression
نویسنده
چکیده
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 vector regression (ε-SVR) and δ support vector regression (δ-SVR). We performed preliminary tests comparing the proposed method with SVR on real world data sets and achieved the improved generalization performance for suboptimal values of ε and δ with the similar overall generalization performance.
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