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» On the Complexity of Function Learning
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ICML
2009
IEEE
16 years 5 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
NIPS
1992
15 years 5 months ago
Feudal Reinforcement Learning
This paper describes the adaption and application of an algorithm called Feudal Reinforcement Learning to a complex gridworld navigation problem. The algorithm proved to be not ea...
Peter Dayan, Geoffrey E. Hinton
150
Voted
PAMI
2010
276views more  PAMI 2010»
15 years 3 months ago
Local-Learning-Based Feature Selection for High-Dimensional Data Analysis
—This paper considers feature selection for data classification in the presence of a huge number of irrelevant features. We propose a new feature selection algorithm that addres...
Yijun Sun, Sinisa Todorovic, Steve Goodison
CORR
2010
Springer
152views Education» more  CORR 2010»
15 years 4 months ago
Neuroevolutionary optimization
Temporal difference methods are theoretically grounded and empirically effective methods for addressing reinforcement learning problems. In most real-world reinforcement learning ...
Eva Volná
ESWA
2007
127views more  ESWA 2007»
15 years 4 months ago
Clustering support vector machines for protein local structure prediction
Understanding the sequence-to-structure relationship is a central task in bioinformatics research. Adequate knowledge about this relationship can potentially improve accuracy for ...
Wei Zhong, Jieyue He, Robert W. Harrison, Phang C....