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, ...
Traditional association mining algorithms use a strict definition of support that requires every item in a frequent itemset to occur in each supporting transaction. In real-life d...
Rohit Gupta, Gang Fang, Blayne Field, Michael Stei...
The collaborative filtering approach to recommender systems predicts user preferences for products or services by learning past useritem relationships. In this work, we propose no...
This work introduces distance-based criteria for segmentation of object trajectories. Segmentation leads to simplification of the original objects into smaller, less complex primi...
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 ...