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» Bottom-Up Learning of Markov Network Structure
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KDD
2009
ACM
191views Data Mining» more  KDD 2009»
14 years 5 months ago
Scalable pseudo-likelihood estimation in hybrid random fields
Learning probabilistic graphical models from high-dimensional datasets is a computationally challenging task. In many interesting applications, the domain dimensionality is such a...
Antonino Freno, Edmondo Trentin, Marco Gori
WWW
2008
ACM
14 years 5 months ago
As we may perceive: finding the boundaries of compound documents on the web
This paper considers the problem of identifying on the Web compound documents (cDocs) ? groups of web pages that in aggregate constitute semantically coherent information entities...
Pavel Dmitriev
SIGSOFT
2009
ACM
14 years 6 months ago
Improving bug triage with bug tossing graphs
A bug report is typically assigned to a single developer who is then responsible for fixing the bug. In Mozilla and Eclipse, between 37%-44% of bug reports are "tossed" ...
Gaeul Jeong, Sunghun Kim, Thomas Zimmermann
TSMC
1998
91views more  TSMC 1998»
13 years 4 months ago
Toward the border between neural and Markovian paradigms
— A new tendency in the design of modern signal processing methods is the creation of hybrid algorithms. This paper gives an overview of different signal processing algorithms si...
Piotr Wilinski, Basel Solaiman, A. Hillion, W. Cza...
AAAI
2000
13 years 6 months ago
Decision Making under Uncertainty: Operations Research Meets AI (Again)
Models for sequential decision making under uncertainty (e.g., Markov decision processes,or MDPs) have beenstudied in operations research for decades. The recent incorporation of ...
Craig Boutilier