www.themegallery.com Kernel design by meta-features We can use meta-feature to create a better prior on w features with similar meta-feature are expected to be similar in weights, i. e, the weights would be a smooth function of the meta-features Use a Gaussian prior on w, defined by a covariance matrix C, and the covariance between a pair of weights is taken to be a decreasing function of the distance between their meta-features Cii=C(ui,ui)Company name www.themegallery.com Kernel design by meta-features We can use meta-feature to create a better prior on w : features with similar meta-feature are expected to be similar in weights, i.e., the weights would be a smooth function of the meta-features. Use a Gaussian prior on w, defined by a covariance matrix C, and the covariance between a pair of weights is taken to be a decreasing function of the distance between their meta-features