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正则化算子与支持向量核之间的联系。

The connection between regularization operators and support vector kernels.

作者信息

Smola Alex J., Schölkopf Bernhard, Müller Klaus Robert

机构信息

GMD First, Rudower Chaussee 5, 12489, Berlin, Germany

出版信息

Neural Netw. 1998 Jun;11(4):637-649. doi: 10.1016/s0893-6080(98)00032-x.

Abstract

In this paper a correspondence is derived between regularization operators used in regularization networks and support vector kernels. We prove that the Green's Functions associated with regularization operators are suitable support vector kernels with equivalent regularization properties. Moreover, the paper provides an analysis of currently used support vector kernels in the view of regularization theory and corresponding operators associated with the classes of both polynomial kernels and translation invariant kernels. The latter are also analyzed on periodical domains. As a by-product we show that a large number of radial basis functions, namely conditionally positive definite functions, may be used as support vector kernels.

摘要

本文推导了正则化网络中使用的正则化算子与支持向量核之间的对应关系。我们证明,与正则化算子相关的格林函数是具有等效正则化性质的合适支持向量核。此外,本文从正则化理论的角度对当前使用的支持向量核以及与多项式核和平移不变核类别相关的相应算子进行了分析。还在周期域上对后者进行了分析。作为一个副产品,我们表明大量的径向基函数,即条件正定函数,可以用作支持向量核。

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