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192
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ICMLA
2010
15 years 4 months ago
2010
Learning the reward function of an agent by observing its behavior is termed inverse reinforcement learning and has applications in learning from demonstration or apprenticeship l...
164
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ICML
2005
IEEE
16 years 7 months ago
2005
IEEE
The integration of diverse forms of informative data by learning an optimal combination of base kernels in classification or regression problems can provide enhanced performance w...
175
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ICML
2009
IEEE
16 years 1 months ago
2009
IEEE
In classification with monotonicity constraints, it is assumed that the class label should increase with increasing values on the attributes. In this paper we aim at formalizing ...
138
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COLT
2000
Springer
15 years 10 months ago
2000
Springer
We model reinforcement learning as the problem of learning to control a Partially Observable Markov Decision Process ( ¢¡¤£¦¥§ ), and focus on gradient ascent approache...
156
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ECML
2006
Springer
15 years 10 months ago
2006
Springer
In many graph-based semi-supervised learning algorithms, edge weights are assumed to be fixed and determined by the data points' (often symmetric) relationships in input space...