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Learning about the Learning Process

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GAMA Joao KOSINA Petr

Rok publikování 2011
Druh Článek ve sborníku
Konference Advances in Intelligent Data Analysis X
Fakulta / Pracoviště MU

Fakulta informatiky

Citace
www http://dx.doi.org/10.1007/978-3-642-24800-9_17
Doi http://dx.doi.org/10.1007/978-3-642-24800-9_17
Obor Informatika
Klíčová slova Data streams; concept drift; meta-learning; recurrent concepts
Popis 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-diagnose 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-learners can detect reccurrence of contexts using unlabeled examples, and take pro-active actions by activating previously learned models.
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