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The Evolution of Blackboard Control Architectures
The Evolution of Blackboard Control Architectures
Execution engine of meta-learning system for KDD in multi-agent environment
AIS-ADM 2005 Proceedings of the 2005 international conference on Autonomous Intelligent Systems: agents and Data Mining
A Novel Meta Learning System and Its Application to Optimization of Computing Agents' Results
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 02
A survey of intelligent assistants for data analysis
ACM Computing Surveys (CSUR)
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Meta-learning has been accepted, in the last five years, as a proper machine learning research field. In this concrete area of interest, the way in which different theories, each one produced either with the same algorithm or with many of them, are merged to produce a more accurate model has been the main topic. Now, new emerging techniques got more to do with inductive meta-learning. It is the process of learning from others learning experiences. This kind of learning imposes severe requisites, from the point of view of the software system that would support it. The purpose of this work is to show a software architecture for this type of learning. The architecture will give recommendations for building a system of this kind, that has to tackle with very precise but difficult problems at a time.