Titlebook: Realtime Data Mining; Self-Learning Techni Alexander Paprotny,Michael Thess Book 2013 Springer International Publishing Switzerland 2013 Ma

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Osteoarthritis

Decomposition in Transition: Adaptive Matrix Factorization,s on real-world data. Moreover, we address a compressive sensing-based approach to Netflix-like matrix completion problems and conclude the chapter by proposing a remedy to complexity issues in computing large elements of the low-rank matrices, which, as we shall see, is a recurring problem related to factorization-based prediction methods.

Atheroma

The Big Picture: Toward a Synthesis of RL and Adaptive Tensor Factorization,ucker-based approximation architecture that relies crucially on the notion of an aggregation basis described in Chap. .. As our method requires a partitioning of the set of state transition histories, we are left with the challenge of how to determine a suitable partitioning, for which we propose a genetic algorithm.

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Brave New Realtime World: Introduction,ime analytics methods. We emphasize that such online learning does not conflict with conventional offline learning but, on the opposite, both complement each other. Finally, we give some methodical remarks.

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Up the Down Staircase: Hierarchical Reinforcement Learning,points; we proceed to devising and justifying approaches to apply hierarchical methods to both the model-based as well as the model-free case. In regard to the latter, we set out from the multigrid reinforcement learning algorithms introduced by Ziv in [Ziv04] and extend these methods to finite-horizon problems.

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