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» Machine Learning by Function Decomposition
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PKDD
2009
Springer
144views Data Mining» more  PKDD 2009»
15 years 6 months ago
Compositional Models for Reinforcement Learning
Abstract. Innovations such as optimistic exploration, function approximation, and hierarchical decomposition have helped scale reinforcement learning to more complex environments, ...
Nicholas K. Jong, Peter Stone
ML
2002
ACM
100views Machine Learning» more  ML 2002»
14 years 11 months ago
Structure in the Space of Value Functions
Solving in an efficient manner many different optimal control tasks within the same underlying environment requires decomposing the environment into its computationally elemental ...
David J. Foster, Peter Dayan
BMCBI
2010
125views more  BMCBI 2010»
14 years 12 months ago
Active machine learning for transmembrane helix prediction
Background: About 30% of genes code for membrane proteins, which are involved in a wide variety of crucial biological functions. Despite their importance, experimentally determine...
Hatice U. Osmanbeyoglu, Jessica A. Wehner, Jaime G...
BMCBI
2010
179views more  BMCBI 2010»
14 years 12 months ago
A semi-supervised learning approach to predict synthetic genetic interactions by combining functional and topological properties
Background: Genetic interaction profiles are highly informative and helpful for understanding the functional linkages between genes, and therefore have been extensively exploited ...
Zhuhong You, Zheng Yin, Kyungsook Han, De-Shuang H...
ALT
2000
Springer
15 years 8 months ago
On the Noise Model of Support Vector Machines Regression
Abstract. Support Vector Machines Regression (SVMR) is a learning technique where the goodness of fit is measured not by the usual quadratic loss function (the mean square error),...
Massimiliano Pontil, Sayan Mukherjee, Federico Gir...