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106
Voted
ECML
2004
Springer
15 years 7 months ago
2004
Springer
Abstract. We investigate the problem of using function approximation in reinforcement learning where the agent’s policy is represented as a classifier mapping states to actions....
133
Voted
PR
2006
15 years 2 months ago
2006
In the past few years, some nonlinear dimensionality reduction (NLDR) or nonlinear manifold learning methods have aroused a great deal of interest in the machine learning communit...
103
Voted
MCS
2002
Springer
15 years 1 months ago
2002
Springer
Abstract. Boosting methods are known to improve generalization performances of learning algorithms reducing both bias and variance or enlarging the margin of the resulting multi-cl...
112
Voted
ICML
2008
IEEE
16 years 3 months ago
2008
IEEE
We propose Laplace max-margin Markov networks (LapM3 N), and a general class of Bayesian M3 N (BM3 N) of which the LapM3 N is a special case with sparse structural bias, for robus...
153
Voted
KDD
2007
ACM
16 years 2 months ago
2007
ACM
A wide variety of machine learning problems can be described as minimizing a regularized risk functional, with different algorithms using different notions of risk and different r...