e-Distance Weighted Support Vector Regression
This work addresses specific challenges in support vector regression for machine learning applications, but appears incremental as it builds on existing methods.
The authors tackled noisy data and boundary data distribution issues in support vector regression by proposing e-DWSVR, which optimizes the minimum margin and mean functional margin, achieving promising results on benchmark datasets.
We propose a novel support vector regression approach called e-Distance Weighted Support Vector Regression (e-DWSVR).e-DWSVR specifically addresses two challenging issues in support vector regression: first, the process of noisy data; second, how to deal with the situation when the distribution of boundary data is different from that of the overall data. The proposed e-DWSVR optimizes the minimum margin and the mean of functional margin simultaneously to tackle these two issues. In addition, we use both dual coordinate descent (CD) and averaged stochastic gradient descent (ASGD) strategies to make e-DWSVR scalable to large scale problems. We report promising results obtained by e-DWSVR in comparison with existing methods on several benchmark datasets.