DOI | Resolve DOI: https://doi.org/10.1007/978-3-642-40994-3_3 |
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Author | Search for: Abbasian, Houman; Search for: Drummond, Chris1; Search for: Japkowicz, Nathalie; Search for: Matwin, Stan |
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Affiliation | - National Research Council of Canada. Information and Communication Technologies
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Format | Text, Book Chapter |
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Conference | European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2013), September 23-27, 2013, Prague, Czech Republic |
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Subject | comprehensibility; different class; ensemble methods; inner ensembles; K-means; K-means clustering; Bayesian networks; learning systems; learning algorithms |
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Abstract | Ensemble 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. |
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Publication date | 2013 |
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Publisher | Springer Berlin Heidelberg |
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In | |
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Series | |
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Language | English |
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Peer reviewed | Yes |
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NPARC number | 21270682 |
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Export citation | Export as RIS |
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Report a correction | Report a correction (opens in a new tab) |
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Record identifier | 909de543-2d40-4e2b-a7b2-c3f6cc1a1873 |
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Record created | 2014-02-17 |
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Record modified | 2020-06-18 |
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