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ATAL
2007
Springer

A framework for agent-based distributed machine learning and data mining

12 years 2 months ago
A framework for agent-based distributed machine learning and data mining
This paper proposes a framework for agent-based distributed machine learning and data mining based on (i) the exchange of meta-level descriptions of individual learning processes among agents and (ii) online reasoning about learning success and learning progress by learning agents. nt an abstract architecture that enables agents to exchange models of their local learning processes and introduces a number of different methods for integrating these processes. This allows us to apply existing agent interaction mechanisms to distributed machine learning tasks, thus leveraging the powerful coordination methods available in agent-based computing, and enables agents to engage in meta-reasoning about their own learning decisions. We apply this architecture to a real-world distributed clustering application to illustrate how the conceptual framework can be used in practical systems in which different learners may be using different datasets, hypotheses and learning algorithms. We report on ...
Jan Tozicka, Michael Rovatsos, Michal Pechoucek
Added 07 Jun 2010
Updated 07 Jun 2010
Type Conference
Year 2007
Where ATAL
Authors Jan Tozicka, Michael Rovatsos, Michal Pechoucek
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