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ECML
2004
Springer
16 years 7 days ago
Model Approximation for HEXQ Hierarchical Reinforcement Learning
HEXQ is a reinforcement learning algorithm that discovers hierarchical structure automatically. The generated task hierarchy repthe problem at different levels of abstraction. In ...
Bernhard Hengst
COLT
2003
Springer
16 years 2 days ago
Learning from Uncertain Data
The application of statistical methods to natural language processing has been remarkably successful over the past two decades. But, to deal with recent problems arising in this ï¬...
Mehryar Mohri
NIPS
2007
15 years 8 months ago
Sparse Feature Learning for Deep Belief Networks
Unsupervised learning algorithms aim to discover the structure hidden in the data, and to learn representations that are more suitable as input to a supervised machine than the ra...
Marc'Aurelio Ranzato, Y-Lan Boureau, Yann LeCun
CONCUR
2010
Springer
15 years 8 months ago
Learning I/O Automata
Links are established between three widely used modeling frameworks for reactive systems: the ioco theory of Tretmans, the interface automata of De Alfaro and Henzinger, and Mealy ...
Fides Aarts, Frits W. Vaandrager
CORR
2006
Springer
130views Education» more  CORR 2006»
15 years 6 months ago
Genetic Programming for Kernel-based Learning with Co-evolving Subsets Selection
Abstract. Support Vector Machines (SVMs) are well-established Machine Learning (ML) algorithms. They rely on the fact that i) linear learning can be formalized as a well-posed opti...
Christian Gagné, Marc Schoenauer, Mich&egra...