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» Hierarchical Hidden Markov Models for Information Extraction
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PAMI
2002
108views more  PAMI 2002»
15 years 1 months ago
Approximate Bayes Factors for Image Segmentation: The Pseudolikelihood Information Criterion (PLIC)
We propose a method for choosing the number of colors or true gray levels in an image; this allows fully automatic segmentation of images. Our underlying probability model is a hid...
Derek C. Stanford, Adrian E. Raftery
ICML
2007
IEEE
16 years 2 months ago
Multi-task reinforcement learning: a hierarchical Bayesian approach
We consider the problem of multi-task reinforcement learning, where the agent needs to solve a sequence of Markov Decision Processes (MDPs) chosen randomly from a fixed but unknow...
Aaron Wilson, Alan Fern, Soumya Ray, Prasad Tadepa...
CIKM
2008
Springer
15 years 3 months ago
Academic conference homepage understanding using constrained hierarchical conditional random fields
We address the problem of academic conference homepage understanding for the Semantic Web. This problem consists of three labeling tasks - labeling conference function pages, func...
Xin Xin, Juanzi Li, Jie Tang, Qiong Luo
ICASSP
2008
IEEE
15 years 8 months ago
Extracting question/answer pairs in multi-party meetings
Understanding multi-party meetings involves tasks such as dialog act segmentation and tagging, action item extraction, and summarization. In this paper we introduce a new task for...
Andreas Kathol, Gökhan Tür
ICPR
2010
IEEE
15 years 5 months ago
Scene Text Extraction with Edge Constraint and Text Collinearity
In this paper, we propose a framework for isolating text regions from natural scene images. The main algorithm has two functions: it generates text region candidates, and it veriï...
Seonghun Lee, Kyomin Jung, Jin Hyung Kim