A fast leave-one-out cross-validation for SVM-like family. Issue 6 (August 2016) - Zhang, Jingxiang; Wang, Shitong
Abstract The leave-one-out cross-validation is an important parameter selection strategy for SVM-like family, including SVM and SVR. However, due to the high computational complexity, the adaptability of this strategy is restricted. In this paper, aiming at its practical application, a fast leave-one-out cross-validation method by using an adjustment factor is proposed which focusses especially on the practicability for the SVM-like family where the decision function can be expressed by explicit dot-product of each training sample pair. The ability of the proposed method in fast parameter selection is better than that of original leave-one-out cross-validation with the same or comparable learning performance. The simulation results indicate the effectiveness and speedup of the proposed leave-one-out cross-validation method.