Expressing web page content in a way that computers can understand is the key to a semantic web. Generating ontological information from the web automatically using machine learni...
Methods for learning Bayesian networks can discover dependency structure between observed variables. Although these methods are useful in many applications, they run into computat...
Eran Segal, Dana Pe'er, Aviv Regev, Daphne Koller,...
We develop a semi-supervised learning method that constrains the posterior distribution of latent variables under a generative model to satisfy a rich set of feature expectation c...
Abstract. Networked multi-agent systems are comprised of many autonomous yet interdependent agents situated in a virtual social network. Two examples of such systems are supply cha...
This paper considers the regularized learning algorithm associated with the leastsquare loss and reproducing kernel Hilbert spaces. The target is the error analysis for the regres...