In this paper, we focus on classifying documents according to opinion and value judgment they contain. The main originality of our approach is to combine linguistic pre-processing,...
We address the problem of classification in partially labeled networks (a.k.a. within-network classification) where observed class labels are sparse. Techniques for statistical re...
Brian Gallagher, Hanghang Tong, Tina Eliassi-Rad, ...
We introduce a system to address the challenges involved in managing the multidimensional sensor data streams generated within immersive environments. We call this data type, imme...
Published data is prone to privacy attacks. Sanitization methods aim to prevent these attacks while maintaining usefulness of the data for legitimate users. Quantifying the trade-...
Kernel machines have been shown as the state-of-the-art learning techniques for classification. In this paper, we propose a novel general framework of learning the Unified Kernel ...