Large-scale support vector regression with budgeted stochastic gradient descent

Zongxia Xie, Yingda Li

Abstract: Support vector regression (SVR) is a widely used regression technique for its competent performance. However, non-linear SVR is time consuming for large-scale tasks due to the dimension curse of kernelization. Recently, a budgeted stochastic gradient descent (BSGD) method has been developed to train large-scale kernelized SVC. In this paper, we extend the BSGD method to non-linear regression tasks. According to the performance of different budget maintenance strategies, we combine the stochastic gradient descent (SGD) method with the merging strategy. Experimental results on real-world datasets show that the proposed kernelized SVR with BSGD can achieve competent accuracy, with good computational efficiency compared to some state-of-the-art algorithms.