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

Oafishness

Building a Recommendation Engine: The XELOPES Library,he introduction of agents. The agent framework is further specified for reinforcement learning, and based on RL we next propose a framework for adaptive recommendation engines. At the end, we briefly discuss the application of XELOPES for real recommendation engines.

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樹(shù)木中

Cervical-Spine

Brave New Realtime World: Introduction,al analytics methods, which learn only from historical data. In particular, we stress the difficulties in the development of theoretically sound realtime analytics methods. We emphasize that such online learning does not conflict with conventional offline learning but, on the opposite, both compleme

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罐里有戒指

Heresy

友好

How Engines Learn to Generate Recommendations: Adaptive Learning Algorithms, that this is an extremely complex problem. The central result is a simple empirical assumption that allows reducing the complexity of the estimation in a way that is computationally suitable to most practical problems. The discussion of this approach gives a deeper insight into essential principles

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Up the Down Staircase: Hierarchical Reinforcement Learning,ines..After providing a general introduction, we approach the framework of hierarchical methods from both the historical analytical and algebraic viewpoints; we proceed to devising and justifying approaches to apply hierarchical methods to both the model-based as well as the model-free case. In rega

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