Learning to cope with domain change has been known
as a challenging problem in many real-world applications.
This paper proposes a novel and efficient approach, named
domain ada...
Yu-Gang Jiang, Jun Wang, Shih-Fu Chang, Chong-Wah ...
In many image and video collections, we have access
only to partially labeled data. For example, personal photo
collections often contain several faces per image and a caption
t...
Benjamin Sapp, Benjamin Taskar, Chris Jordan, Timo...
There has been a recent, growing interest in classification and link prediction in structured domains. Methods such as conditional random fields and relational Markov networks sup...
Most up-to-date well-behaved topic-based summarization systems are built upon the extractive framework. They score the sentences based on the associated features by manually assig...
In this paper we explore the use of several types of structural restrictions within algorithms for learning Bayesian networks. These restrictions may codify expert knowledge in a g...