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» Hierarchical Hidden Markov Models for Information Extraction
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WWW
2001
ACM
16 years 2 months ago
Crawling the Hidden Web
Current-day crawlers retrieve content only from the publicly indexable Web, i.e., the set of Web pages reachable purely by following hypertext links, ignoring search forms and pag...
Sriram Raghavan, Hector Garcia-Molina
AAAI
2006
15 years 2 months ago
Unifying Logical and Statistical AI
Intelligent agents must be able to handle the complexity and uncertainty of the real world. Logical AI has focused mainly on the former, and statistical AI on the latter. Markov l...
Pedro Domingos, Stanley Kok, Hoifung Poon, Matthew...
CVPR
2008
IEEE
15 years 3 months ago
Extracting smooth and transparent layers from a single image
Layer decomposition from a single image is an underconstrained problem, because there are more unknowns than equations. This paper studies a slightly easier but very useful altern...
Sai Kit Yeung, Tai-Pang Wu, Chi-Keung Tang
NIPS
2008
15 years 2 months ago
Partially Observed Maximum Entropy Discrimination Markov Networks
Learning graphical models with hidden variables can offer semantic insights to complex data and lead to salient structured predictors without relying on expensive, sometime unatta...
Jun Zhu, Eric P. Xing, Bo Zhang
133
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ICML
2005
IEEE
16 years 2 months ago
Recognition and reproduction of gestures using a probabilistic framework combining PCA, ICA and HMM
This paper explores the issue of recognizing, generalizing and reproducing arbitrary gestures. We aim at extracting a representation that encapsulates only the key aspects of the ...
Sylvain Calinon, Aude Billard