Kernel Methods and Radical-Basis Function Networks

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.

评论
添加红包

请填写红包祝福语或标题

红包个数最小为10个

红包金额最低5元

当前余额3.43前往充值 >
需支付:10.00
成就一亿技术人!
领取后你会自动成为博主和红包主的粉丝 规则
hope_wisdom
发出的红包
实付
使用余额支付
点击重新获取
扫码支付
钱包余额 0

抵扣说明:

1.余额是钱包充值的虚拟货币,按照1:1的比例进行支付金额的抵扣。
2.余额无法直接购买下载,可以购买VIP、付费专栏及课程。

余额充值