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We present a reinforcement learning approach to elicit individualized dose adjustment policies for patients suffering anemia due to end stage renal disease. Our goal is to achieve stable steady-state anemia management in patients with exhibiting different levels of treatment response. The approach uses Q-learning with parsimonious parametric representation of the state-action value function. We show...
Semi-supervised classification methods aim to exploit labelled and unlabelled examples to train a predictive model. Most of these approaches make assumptions on the distribution of classes. This article first proposes a new semi-supervised discretization method which adopts very low informative prior on data. This method discretizes the numerical domain of a continuous input variable, while keeping...
Clustering high dimensional data is a big challenge in data mining due to the curse of dimensionality. To solve this problem, projective clustering has been defined as an extension of traditional clustering that seeks to find projected clusters in subsets of dimensions of a data space. In this paper, the problem of modeling projected clusters is first discussed, and an extended Gaussian model is proposed...
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