Kernel Methods and Radical-Basis Function Networks

最新推荐文章于 2021-07-16 20:07:11 发布

原创最新推荐文章于 2021-07-16 20:07:11 发布 · 403 阅读

0 ·

CC 4.0 BY-SA版权

本文探讨了核方法(kernel methods)的基本概念及其如何通过映射到高维空间实现非线性分类任务。此外，还介绍了Cover定理，该定理阐述了在高维空间中模式分类问题更有可能被线性分离的原理。

Kernel Methods

Kernelization

a linear classifier in a higher-dimensional space corresponds to a non-linear classifier in the original space.
this is akin to making regression more flexible by using polynomials.
A potential drawback:we might increase the computational burden since the expandation of dimension.

Chap5.2 Cover’s Theorem on the separability of patterns

Cover Theorem: A complex pattern-classification problem, cast in a high-dimensional space nonlinearly, is more likely to be linearly saparable than in a low-dimensional space, provided that the space is not densely populated.