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AAAI
2008
15 years 7 months ago
Potential-based Shaping in Model-based Reinforcement Learning
Potential-based shaping was designed as a way of introducing background knowledge into model-free reinforcement-learning algorithms. By identifying states that are likely to have ...
John Asmuth, Michael L. Littman, Robert Zinkov
ACSC
2005
IEEE
15 years 7 months ago
Gradiance On-Line Accelerated Learning
Gradiance On-Line Accelerated Learning GOAL is a system for creating and automatically grading homeworks, programming laboratories, and tests. Through the concept of root questi...
Jeffrey D. Ullman
PR
2006
93views more  PR 2006»
15 years 5 months ago
Learning the kernel parameters in kernel minimum distance classifier
Choosing appropriate values for kernel parameters is one of the key problems in many kernel-based methods because the values of these parameters have significant impact on the per...
Daoqiang Zhang, Songcan Chen, Zhi-Hua Zhou
ICCV
2007
IEEE
16 years 7 months ago
Boosting Invariance and Efficiency in Supervised Learning
In this paper we present a novel boosting algorithm for supervised learning that incorporates invariance to data transformations and has high generalization capabilities. While on...
Andrea Vedaldi, Paolo Favaro, Enrico Grisan
DAC
2008
ACM
16 years 6 months ago
Temperature management in multiprocessor SoCs using online learning
In deep submicron circuits, thermal hot spots and high temperature gradients increase the cooling costs, and degrade reliability and performance. In this paper, we propose a low-co...
Ayse Kivilcim Coskun, Tajana Simunic Rosing, Kenny...