We present a general approach to model selection and regularization that exploits unlabeled data to adaptively control hypothesis complexity in supervised learning tasks. The idea ...
We consider the problem of improving the efficiency of query processing on an XML interface of a relational database, for predefined query workloads. The main contribution of this ...
We describe a new scalable algorithm for semi-supervised training of conditional random fields (CRF) and its application to partof-speech (POS) tagging. The algorithm uses a simil...
We address the problem of segmenting 3D scan data into objects or object classes. Our segmentation framework is based on a subclass of Markov Random Fields (MRFs) which support ef...
Dragomir Anguelov, Benjamin Taskar, Vassil Chatalb...
The transition of search engine usersā intents has been studied for a long time. The knowledge of intent transition, once discovered, can yield a better understanding of how diļ...