Covariance kernels from Bayesian generative models

@misc{citeulike:2636569, abstract = {We propose the framework of mutual information kernels for learning covariance kernels, as used in Support Vector machines and Gaussian process classifiers, from unlabeled task data using Bayesian techniques. We describe an implementation of this framework which uses variational Bayesian mixtures of factor analyzers in order to attack classification problems in high-dimensional spaces where labeled data is sparse, but unlabeled data is abundant.}, author = {Seeger, M. }, citeulike-article-id = {2636569}, keywords = {bayesian, gaussian\_process, generative\_models, semi\_supervised}, posted-at = {2008-04-07 06:11:12}, priority = {0}, title = {Covariance kernels from Bayesian generative models}, url = {http://citeseer.ist.psu.edu/541863.html}, year = {2000} }

TY - GEN ID - citeulike:2636569 L3 - citeulike-article-id:2636569 TI - Covariance kernels from Bayesian generative models N2 - We propose the framework of mutual information kernels for learning covariance kernels, as used in Support Vector machines and Gaussian process classifiers, from unlabeled task data using Bayesian techniques. We describe an implementation of this framework which uses variational Bayesian mixtures of factor analyzers in order to attack classification problems in high-dimensional spaces where labeled data is sparse, but unlabeled data is abundant. KW - bayesian KW - gaussian_process KW - generative_models KW - semi_supervised AU - Seeger, M PY - 2000/// UR - http://citeseer.ist.psu.edu/541863.html ER -