高斯过程回归及分类的代码，内容全，有实例，注释清晰。包括分类系列和预测回归系列，值得感兴趣的同学学习借鉴。里面有对应的数据和demo程序，程序可运行，MATLAB2014a下测试通过，其他版本没有测试。（网页版的0

机器学习算法之线性回归 最小二乘法和岭回归算法的实现，对应博文为： http://blog.csdn.net/suipingsp/article/details/42101139

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.