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Erschienen in:
2011 | OriginalPaper | Buchkapitel
Learning about the Learning Process
verfasst von : João Gama, Petr Kosina
Erschienen in: Advances in Intelligent Data Analysis X
Verlag: Springer Berlin Heidelberg
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This work addresses the problem of mining data stream generated in dynamic environments where the distribution underlying the observations may change over time. We present a system that monitors the evolution of the learning process. The system is able to self-diagnosis degradations of this process, using change detection mechanisms, and self-repairs the decision models. The system uses meta-learning techniques that characterize the domain of applicability of previously learned models. The meta-learns can detect re-occurrence of contexts, using unlabeled examples, and take pro-active actions by activating previously learned models.