http:/parnec.nuaa.edu.cn Kernel design by meta-features In the standard approach of linear SVM, we solve w:(wx)y≥1,i=1,…,m which can be viewed as finding the maximum-likelihood hypothesis, under the above constraint, where we have a gaussian prior on w P(w)xe 去w2C-1 The covariance matrix C equals the unit matrix, i.e. all weights are assumed to be independent and have the same varianceCompany name www.themegallery.com Kernel design by meta-features In the standard approach of linear SVM, we solve which can be viewed as finding the maximum-likelihood hypothesis, under the above constraint, where we have a Gaussian prior on w The covariance matrix C equals the unit matrix, i.e. all weights are assumed to be independent and have the same variance