Abstract. We consider the problem of training discriminative structured output predictors, such as conditional random fields (CRFs) and structured support vector machines (SSVMs)....
Patrick Pletscher, Cheng Soon Ong, Joachim M. Buhm...
Learning patterns of human behavior from sensor data is extremely important for high-level activity inference. We show how to extract a person’s activities and significant plac...
While conditional random fields (CRFs) have been applied successfully in a variety of domains, their training remains a challenging task. In this paper, we introduce a novel trai...
Lin Liao, Tanzeem Choudhury, Dieter Fox, Henry A. ...
The ability to find tables and extract information from them is a necessary component of data mining, question answering, and other information retrieval tasks. Documents often c...
David Pinto, Andrew McCallum, Xing Wei, W. Bruce C...
Abstract—Due to their ability to model sequential data without making unnecessary independence assumptions, conditional random fields (CRFs) have become an increasingly popular ...
Surveillance systems that operate continuously generate large volumes of data. One such system is described here, continuously tracking and storing observations taken from multiple...
Abstract— Temporal classification, such as activity recognition, is a key component for creating intelligent robot systems. In the case of robots, classification algorithms mus...
Douglas L. Vail, John D. Lafferty, Manuela M. Velo...
Automatic segmentation and classification of recorded meetings provides a basis towards understanding the content of a meeting. It enables effective browsing and querying in a me...
— We investigate modeling and recognition of object manipulation actions for the purpose of imitation based learning in robotics. To model the process, we are using a combination...
When search is against structured documents, it is beneficial to extract information from user queries in a format that is consistent with the backend data structure. As one step...