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AAAI
2011
12 years 9 months ago
Size Adaptive Selection of Most Informative Features
In this paper, we propose a novel method to select the most informative subset of features, which has little redundancy and very strong discriminating power. Our proposed approach...
Si Liu, Hairong Liu, Longin Jan Latecki, Shuicheng...
AAAI
2006
13 years 10 months ago
Robust Support Vector Machine Training via Convex Outlier Ablation
One of the well known risks of large margin training methods, such as boosting and support vector machines (SVMs), is their sensitivity to outliers. These risks are normally mitig...
Linli Xu, Koby Crammer, Dale Schuurmans
CP
2007
Springer
14 years 3 months ago
Structural Relaxations by Variable Renaming and Their Compilation for Solving MinCostSAT
Searching for optimal solutions to a problem using lower bounds obtained from a relaxation is a common idea in Heuristic Search and Planning. In SAT and CSPs, however, explicit rel...
Miquel Ramírez, Hector Geffner
STOC
2006
ACM
132views Algorithms» more  STOC 2006»
14 years 9 months ago
Near-optimal algorithms for unique games
Unique games are constraint satisfaction problems that can be viewed as a generalization of Max-Cut to a larger domain size. The Unique Games Conjecture states that it is hard to ...
Moses Charikar, Konstantin Makarychev, Yury Makary...
AAAI
2006
13 years 10 months ago
Compact, Convex Upper Bound Iteration for Approximate POMDP Planning
Partially observable Markov decision processes (POMDPs) are an intuitive and general way to model sequential decision making problems under uncertainty. Unfortunately, even approx...
Tao Wang, Pascal Poupart, Michael H. Bowling, Dale...