One of the major strengths of probabilistic topic modeling is the ability to reveal hidden relations via the analysis of co-occurrence patterns on dyadic observations, such as docu...
We present novel semi-supervised boosting algorithms that incrementally build linear combinations of weak classifiers through generic functional gradient descent using both labele...
Multi-task learning leverages shared information among data sets to improve the learning performance of individual tasks. The paper applies this framework for data where each task ...
Collaborative filtering is an important topic in data mining and has been widely used in recommendation system. In this paper, we proposed a unified model for collaborative fil...
We consider the problem of template-independent news extraction. The state-of-the-art news extraction method is based on template-level wrapper induction, which has two serious li...
Junfeng Wang, Xiaofei He, Can Wang, Jian Pei, Jiaj...