This paper aims to tackle the very interesting and important problem of user personalized ranking of search results. The focus is on news retrieval and the data from which the ran...
Text classification is the process of classifying documents into predefined categories based on their content. Existing supervised learning algorithms to automatically classify te...
We consider the parallels between the preference elicitation problem in combinatorial auctions and the problem of learning an unknown function from learning theory. We show that l...
In many real-world domains, undirected graphical models such as Markov random fields provide a more natural representation of the dependency structure than directed graphical mode...
Sushmita Roy, Terran Lane, Margaret Werner-Washbur...
This paper presents new and effective algorithms for learning kernels. In particular, as shown by our empirical results, these algorithms consistently outperform the so-called uni...