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ICML
2007
IEEE
14 years 8 months ago
Conditional random fields for multi-agent reinforcement learning
Conditional random fields (CRFs) are graphical models for modeling the probability of labels given the observations. They have traditionally been trained with using a set of obser...
Xinhua Zhang, Douglas Aberdeen, S. V. N. Vishwanat...
ICS
2010
Tsinghua U.
14 years 5 months ago
Distribution-Specific Agnostic Boosting
We consider the problem of boosting the accuracy of weak learning algorithms in the agnostic learning framework of Haussler (1992) and Kearns et al. (1992). Known algorithms for t...
Vitaly Feldman
BMCBI
2010
147views more  BMCBI 2010»
13 years 7 months ago
Learning biological network using mutual information and conditional independence
Background: Biological networks offer us a new way to investigate the interactions among different components and address the biological system as a whole. In this paper, a revers...
Dong-Chul Kim, Xiaoyu Wang, Chin-Rang Yang, Jean G...
DIMVA
2009
13 years 8 months ago
A Service Dependency Modeling Framework for Policy-Based Response Enforcement
The use of dynamic access control policies for threat response adapts local response decisions to high level system constraints. However, security policies are often carefully tigh...
Nizar Kheir, Hervé Debar, Fréd&eacut...
GECCO
2006
Springer
172views Optimization» more  GECCO 2006»
13 years 11 months ago
Multi-objective optimisation of the protein-ligand docking problem in drug discovery
The pharmaceutical industry is facing an ever-increasing demand to discover novel drugs that are more effective and safer than existing ones. The industry faces huge problem in im...
A. Oduguwa, A. Tiwari, S. Fiorentino, R. Roy