Regularization by the Linear Functional Strategy with Multiple Kernels

Frontiers in Applied Mathematics and Statistics

Pereverzyev, Sergei V. V¹  Tkachenko, Pavlo¹ 

[1] Johann Radon Institute for Computational and Applied Mathematics, Linz, Austria

关键词: Linear functional strategy;    Multiple kernel learning;    regularization;    supervised learning;    kernel learning;   

DOI  :  10.3389/fams.2017.00001

学科分类：数学（综合）

来源: Frontiers

【 摘 要 】

The choice of the kernel is known to be a challenging and central problem of kernel based supervised learning. Recent applications and significant amount of literature have shown that using multiple kernels (the so-called Multiple Kernel Learning (MKL)) instead of a single one can enhance the interpretability of the learned function and improve performances. However, a comparison of existing MKL-algorithms shows that though there may not be large differences in terms of accuracy, there is difference between MKL-algorithms in complexity as given by the training time, for example. In this paper we present a promising approach for training the MKL-machine by the linear functional strategy, which is either faster or more accurate than previously known ones.

【 授权许可】

CC BY