Abstract. In this paper, we propose a new dynamic learning framework that requires a small amount of labeled data in the beginning, then incrementally discovers informative unlabel...
Weijun He, Xiaolei Huang, Dimitris N. Metaxas, Xia...
Ranking is at the heart of many information retrieval applications. Unlike standard regression or classification in which we predict outputs independently, in ranking we are inter...
We use clustering to derive new relations which augment database schema used in automatic generation of predictive features in statistical relational learning. Clustering improves...
This paper proposes a novel, unified, and systematic approach to combine collaborative and content-based filtering for ranking and user preference prediction. The framework inco...
We present a large-margin formulation and algorithm for structured output prediction that allows the use of latent variables. Our proposal covers a large range of application prob...