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AAAI
2012
11 years 11 months ago
Kernel-Based Reinforcement Learning on Representative States
Markov decision processes (MDPs) are an established framework for solving sequential decision-making problems under uncertainty. In this work, we propose a new method for batchmod...
Branislav Kveton, Georgios Theocharous
COCOON
2007
Springer
14 years 3 months ago
Streaming Algorithms Measured in Terms of the Computed Quantity
The last decade witnessed the extensive studies of algorithms for data streams. In this model, the input is given as a sequence of items passing only once or a few times, and we ar...
Shengyu Zhang
TALG
2010
158views more  TALG 2010»
13 years 3 months ago
Clustering for metric and nonmetric distance measures
We study a generalization of the k-median problem with respect to an arbitrary dissimilarity measure D. Given a finite set P of size n, our goal is to find a set C of size k such t...
Marcel R. Ackermann, Johannes Blömer, Christi...
ICML
2009
IEEE
14 years 10 months ago
Learning with structured sparsity
This paper investigates a new learning formulation called structured sparsity, which is a naturalextensionofthestandardsparsityconceptinstatisticallearningandcompressivesensing. B...
Junzhou Huang, Tong Zhang, Dimitris N. Metaxas
CVPR
2009
IEEE
1468views Computer Vision» more  CVPR 2009»
15 years 4 months ago
Hardware-Efficient Belief Propagation
Belief propagation (BP) is an effective algorithm for solving energy minimization problems in computer vision. However, it requires enormous memory, bandwidth, and computation beca...
Chao-Chung Cheng, Chia-Kai Liang, Homer H. Chen, L...