Learning the Kernel with Hyperkernels
Learning the Kernel with Hyperkernels
复制标题
DOI：
--
复制
发表时间：
2005-12
期刊：
影响因子：
--
通讯作者：
Cheng Soon Ong;Alex Smola;R. C. Williamson
中科院分区：
文献类型：
--
作者：
Cheng Soon Ong;Alex Smola;R. C. Williamson
本文讨论了如何选择一个适合于用支持向量机进行估计的核，从而进一步实现机器学习的自动化。这个目标是通过在核空间本身上定义一个再生核希尔伯特空间来实现的。这样的配方导致一个统计估计问题类似的问题，最小化一个正则化的风险functional.We国家的核的选择等价表示定理，并提出了一个半定规划公式所产生的优化问题。提供了几种构造超核的方法，以及常见机器学习问题的细节。对UCI数据的分类、回归和新奇检测实验结果表明了该方法的可行性。
This paper addresses the problem of choosing a kernel suitable for estimation with a support vector machine, hence further automating machine learning. This goal is achieved by defining a reproducing kernel Hilbert space on the space of kernels itself. Such a formulation leads to a statistical estimation problem similar to the problem of minimizing a regularized risk functional.We state the equivalent representer theorem for the choice of kernels and present a semidefinite programming formulation of the resulting optimization problem. Several recipes for constructing hyperkernels are provided, as well as the details of common machine learning problems. Experimental results for classification, regression and novelty detection on UCI data show the feasibility of our approach.