LEAST SQUARES SUPPORT VECTOR MACHINES

上传者: painfulresult | 上传时间: 2009-02-19 00:00:00 | 文件大小: 12.09MB | 文件类型: pdf
This book focuses on Least Squares Support Vector Machines (LS-SVMs) which are reformulations to standard SVMs. LS-SVMs are closely related to regularization networks and Gaussian processes but additionally emphasize and exploit primal-dual interpretations from optimization theory. The authors explain the natural links between LS-SVM classifiers and kernel Fisher discriminant analysis. Bayesian inference of LS-SVM models is discussed, together with methods for imposing sparseness and employing robust statistics. The framework is further extended towards unsupervised learning by considering PCA analysis and its kernel version as a one-class modelling problem. This leads to new primal-dual support vector machine formulations for kernel PCA and kernel CCA analysis. Furthermore, LS-SVM formulations are given for recurrent networks and control. In general, support vector machines may pose heavy computational challenges for large data sets. For this purpose, a method of fixed size LS-SVM is proposed where the estimation is done in the primal space in relation to a Nyström sampling with active selection of support vectors. The methods are illustrated with several examples.

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评论信息

  • redeem92 :
    非常好的支持向量机书籍
    2019-06-15
  • xiaoxiaodechao :
    太贵了,有的地方都不要钱就可以下的
    2018-07-05
  • bac13770645283 :
    不错的书,只是有点不全啊,忘补全
    2018-04-13
  • Phoebe_Ma :
    书很好~谢谢楼主~要是补全后边200来页就完美了
    2017-11-07
  • nottwo :
    经典之书,太贵了!
    2016-12-22

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