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ICDM
2007
IEEE
124views Data Mining» more  ICDM 2007»
14 years 1 months ago
Community Learning by Graph Approximation
Learning communities from a graph is an important problem in many domains. Different types of communities can be generalized as link-pattern based communities. In this paper, we p...
Bo Long, Xiaoyun Xu, Zhongfei (Mark) Zhang, Philip...
CVPR
2009
IEEE
15 years 2 months ago
Understanding Videos, Constructing Plots - Learning a Visually Grounded Storyline Model from Annotated Videos
Analyzing videos of human activities involves not only recognizing actions (typically based on their appearances), but also determining the story/plot of the video. The storyline...
Abhinav Gupta (University of Maryland), Praveen Sr...
AAAI
2006
13 years 8 months ago
Learning Representation and Control in Continuous Markov Decision Processes
This paper presents a novel framework for simultaneously learning representation and control in continuous Markov decision processes. Our approach builds on the framework of proto...
Sridhar Mahadevan, Mauro Maggioni, Kimberly Fergus...
EMNLP
2010
13 years 5 months ago
Efficient Graph-Based Semi-Supervised Learning of Structured Tagging Models
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...
Amarnag Subramanya, Slav Petrov, Fernando Pereira
CISS
2008
IEEE
14 years 1 months ago
Distributed estimation in wireless sensor networks via variational message passing
Abstract – In this paper, a variational message passing framework is proposed for Markov random fields. Analogous to the traditional belief propagation algorithm, variational mes...
Yanbing Zhang, Huaiyu Dai