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ICML
2009
IEEE
16 years 7 months ago
Model-free reinforcement learning as mixture learning
We cast model-free reinforcement learning as the problem of maximizing the likelihood of a probabilistic mixture model via sampling, addressing both the infinite and finite horizo...
Nikos Vlassis, Marc Toussaint
ICML
2007
IEEE
16 years 7 months ago
Approximate maximum margin algorithms with rules controlled by the number of mistakes
We present a family of incremental Perceptron-like algorithms (PLAs) with margin in which both the "effective" learning rate, defined as the ratio of the learning rate t...
Petroula Tsampouka, John Shawe-Taylor
ICML
2007
IEEE
16 years 7 months ago
Optimal dimensionality of metric space for classification
In many real-world applications, Euclidean distance in the original space is not good due to the curse of dimensionality. In this paper, we propose a new method, called Discrimina...
Wei Zhang, Xiangyang Xue, Zichen Sun, Yue-Fei Guo,...
ICML
2007
IEEE
16 years 7 months ago
Infinite mixtures of trees
Finite mixtures of tree-structured distributions have been shown to be efficient and effective in modeling multivariate distributions. Using Dirichlet processes, we extend this ap...
Sergey Kirshner, Padhraic Smyth
161
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ICML
2006
IEEE
16 years 7 months ago
Pareto optimal linear classification
We consider the problem of choosing a linear classifier that minimizes misclassification probabilities in two-class classification, which is a bi-criterion problem, involving a tr...
Seung-Jean Kim, Alessandro Magnani, Sikandar Samar...