On-line methods in machine learning

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The proposed research is focused on on-line learning and sequential decision making in stochastic environments. Examples of sequential decision making problems abound: controlling industrial plants, performing clinical trials, managing investments, monitoring pipeline-systems, etc. An on-line learning algorithm improves the performance of a system during its normal operation. In contrast, off-line learning algorithms are trained with a fixed amount of data and then used with learning turned off. In off-line learning the training data has to be fully representative of the system's behaviour or performance may be poor. On-line learning systems overcome this potential weakness because they never stop adapting.