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ACL
1994
15 years 8 months ago
A Markov Language Learning Model for Finite Parameter Spaces
This paper shows how to formally characterize language learning in a finite parameter space as a Markov structure, hnportant new language learning results follow directly: explici...
Partha Niyogi, Robert C. Berwick
ATAL
2006
Springer
15 years 10 months ago
A hierarchical approach to efficient reinforcement learning in deterministic domains
Factored representations, model-based learning, and hierarchies are well-studied techniques for improving the learning efficiency of reinforcement-learning algorithms in large-sca...
Carlos Diuk, Alexander L. Strehl, Michael L. Littm...
ICML
2004
IEEE
16 years 7 months ago
Support vector machine learning for interdependent and structured output spaces
Learning general functional dependencies is one of the main goals in machine learning. Recent progress in kernel-based methods has focused on designing flexible and powerful input...
Ioannis Tsochantaridis, Thomas Hofmann, Thorsten J...
ITCC
2005
IEEE
16 years 13 days ago
Research on DRM-Enabled Learning Objects Model
The paper mainly discusses the DRM-enabled learning object model. Firstly, it analyses the art-of-status of Intellectual Property Rights in e-learning. Secondly, according to appl...
Qingtang Liu, Zongkai Yang, Kun Yan, Jing Jin, Wan...
GECCO
2004
Springer
16 years 7 days ago
Bounding Learning Time in XCS
It has been shown empirically that the XCS classifier system solves typical classification problems in a machine learning competitive way. However, until now, no learning time es...
Martin V. Butz, David E. Goldberg, Pier Luca Lanzi