(机器学习复习资料1)22-Apr 7_Kernel Methods and SVM's(下)。听TED演讲,看国内、国际名校好课,就在网易公开课
Kernel methods have been particularly successful in a variety of areas because they can enable a feature extractor and classifier to learn a complex decision boundary with only a few parameters by projecting the data onto a high-dimensional reproducing kernel Hilbert space. Kernel-basedlearning ...
ScholkopfB., Platt J., Shawe-Taylor J., SmolaA.J. , and Williamson R.C. 2001. Estimating the support of a high-dimensional distribution. Neural Computation, 13(7): 1443–1471 7. 核方法 “表示定理” 人们发展出一系列基于核函数的学习方法,统称为"核方法" (kernel methods). 最常见的,是通...
通过 Kernel 推广到非线性的情况就变成了一件非常容易的事情了(相信,你还记得本节开头所说的:“通过求解对偶问题得到最优解,这就是线性可分条件下支持向量机的对偶算法,这样做的优点在于:一者对偶问题往往更容易求解;二者可以自然的引入核函数,进而推广到非线性分类问题”)。
kernlab - Kernel-based Machine Learning library for R TinySVM -- a small SVM implementation,written in C++ R e1071 - Machine learning library for R Matlab SimpleSVM - SimpleSVM toolbox for Matlab SVM and Kernel Methods Matlab Toolbox
Kernel machine methods in genomics Numerical examples SVM and splinesGhosh, Debashis
kernel & basis expansion(compared) Oxford-Basis Expansion, Regularization, Validation, SNU-Basis expansions and Kernel methods, 类似:使用创建多项式方法创建新特征,都可用于线性分类(线性核),都能升维 不同:feature map不同(Φ(x)Φ(x)),存在非线性核 模型: linearmodel:y=w⋅Φ(x)+ϵbasis:{...
随着研究的发展,SVM被扩展到支持向量回归,甚至扩展至生存分析问题上。使用支持向量机解决生存问题已经提出了三种方法: the regression (Shiv- aswamy et al., 2007), the ranking (Van Belle et al., 2007; Evers and Messow, 2008; Van Belle et al., 2008) ...
当然,到目前为止,我们的 SVM 还比较弱,只能处理线性的情况,不过,在得到了对偶dual 形式之后,通过 Kernel 推广到非线性的情况就变成了一件非常容易的事情了(相信,你还记得本节开头所说的:“通过求解对偶问题得到最优解,这就是线性可分条件下支持向量机的对偶算法,这样做的优点在于:一者对偶问题往往更容易求解;...
[4] C. Cortes and V. Vapnik. Support-vector networks. Machine Learning, 20(3):273–297, 1995. 1 [5] N. Cristianini and J. Shawe-Taylor. An introduction to support vector machines and other kernel-based learning methods. Cambridge University Press, 2000. 6 ...