Inner ensembles : using ensemble methods inside the learning algorithm

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TypeBook Chapter
Proceedings titleMachine Learning and Knowledge Discovery in Databases : European Conference, ECML PKDD 2013, Prague, Czech Republic, September 23-27, 2013, Proceedings, Part III
Series titleLecture Notes In Computer Science; Volume 8190
ConferenceEuropean Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2013), September 23-27, 2013, Prague, Czech Republic
Pages3348; # of pages: 16
SubjectComprehensibility; Different class; Ensemble methods; Inner Ensembles; K-means; K-means clustering; Bayesian networks; Learning systems; Learning algorithms
AbstractEnsemble Methods represent an important research area within machine learning. Here, we argue that the use of such methods can be generalized and applied in many more situations than they have been previously. Instead of using them only to combine the output of an algorithm, we can apply them to the decisions made inside the learning algorithm, itself. We call this approach Inner Ensembles. The main contribution of this work is to demonstrate how broadly this idea can applied. Specifically, we show that the idea can be applied to different classes of learner such as Bayesian networks and K-means clustering. © 2013 Springer-Verlag.
Publication date
PublisherSpringer Berlin Heidelberg
AffiliationInformation and Communication Technologies; National Research Council Canada
Peer reviewedYes
NPARC number21270682
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Record identifier909de543-2d40-4e2b-a7b2-c3f6cc1a1873
Record created2014-02-17
Record modified2016-06-27
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