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» Skill Combination for Reinforcement Learning
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JCAL
2000
80views more  JCAL 2000»
15 years 5 months ago
An activity-based analysis of hands-on practice methods
The success of exploration-based training is likely to be strongly influenced by what activities the learner undertakes during training. This paper presents a study of the activiti...
Susan Wiedenbeck, J. A. Zavala, Jason Nawyn
ICML
2004
IEEE
16 years 6 months ago
Multi-task feature and kernel selection for SVMs
We compute a common feature selection or kernel selection configuration for multiple support vector machines (SVMs) trained on different yet inter-related datasets. The method is ...
Tony Jebara
ITS
1998
Springer
213views Multimedia» more  ITS 1998»
15 years 9 months ago
Component-Based Construction of a Science Learning Space
We present a vision for learning environments, called Science Learning Spaces, that are rich in engaging content and activities, provide constructive experiences in scientific proc...
Kenneth R. Koedinger, Daniel D. Suthers, Kenneth D...
CORR
2010
Springer
152views Education» more  CORR 2010»
15 years 5 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
CI
2005
106views more  CI 2005»
15 years 5 months ago
Incremental Learning of Procedural Planning Knowledge in Challenging Environments
Autonomous agents that learn about their environment can be divided into two broad classes. One class of existing learners, reinforcement learners, typically employ weak learning ...
Douglas J. Pearson, John E. Laird