Dasgupta Sayan, Goldberg Yair, Kosorok Michael R
The University of North Carolina at Chapel Hill.
University of Haifa.
Ann Stat. 2019 Feb;47(1):497-526. doi: 10.1214/18-AOS1696.
We develop an approach for feature elimination in statistical learning with kernel machines, based on recursive elimination of features. We present theoretical properties of this method and show that it is uniformly consistent in finding the correct feature space under certain generalized assumptions. We present a few case studies to show that the assumptions are met in most practical situations and present simulation results to demonstrate performance of the proposed approach.
我们基于特征的递归消除，开发了一种用于核机器统计学习中特征消除的方法。我们给出了该方法的理论性质，并表明在某些广义假设下，它在找到正确特征空间方面是一致收敛的。我们给出了一些案例研究，以表明在大多数实际情况下这些假设是成立的，并给出了模拟结果以证明所提方法的性能。
