| DOI | Trouver le DOI : https://doi.org/10.1007/978-3-642-40994-3_3 |
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| Auteur | Rechercher : Abbasian, Houman; Rechercher : Drummond, Chris1; Rechercher : Japkowicz, Nathalie; Rechercher : Matwin, Stan |
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| Affiliation | - Conseil national de recherches du Canada. Technologies de l'information et des communications
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| Format | Texte, Chapitre de livre |
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| Conférence | 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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| Sujet | comprehensibility; different class; ensemble methods; inner ensembles; K-means; K-means clustering; Bayesian networks; learning systems; learning algorithms |
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| Résumé | 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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| Date de publication | 2013 |
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| Maison d’édition | Springer Berlin Heidelberg |
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| Dans | |
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| Série | |
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| Langue | anglais |
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| Publications évaluées par des pairs | Oui |
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| Numéro NPARC | 21270682 |
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| Exporter la notice | Exporter en format RIS |
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| Signaler une correction | Signaler une correction (s'ouvre dans un nouvel onglet) |
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| Identificateur de l’enregistrement | 909de543-2d40-4e2b-a7b2-c3f6cc1a1873 |
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| Enregistrement créé | 2014-02-17 |
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| Enregistrement modifié | 2020-06-18 |
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