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Characterization of Learning Instances for Evolutionary Meta-Learning

Raynaut, William and Soulé-Dupuy, Chantal and Vallés-Parlangeau, Nathalie and Dray, Cédric and Valet, Philippe Characterization of Learning Instances for Evolutionary Meta-Learning. (2015) In: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2015), 7 September 2015 - 11 September 2015 (Porto, Portugal).

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Abstract

Machine learning has proven to be a powerful tool in diverse fields, and is getting more and more widely used by non-experts. One of the foremost difficulties they encounter lies in the choice and calibration of the machine learning algorithm to use. Our objective is thus to provide assistance in the matter, using a meta-learning approach based on an evolutionary heuristic. We expand here previous work presenting the intended workflow of a modeling assistant by describing the characterization of learning instances we intend to use.

Item Type:Conference or Workshop Item (Paper)
Additional Information:Thanks to European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 2015. This papers appears in Proceedings of the ECMLPKDD 2015 ISBN: 978-952-60-6443-7 The original PDF is available at: https://aaltodoc.aalto.fi/bitstream/handle/123456789/18224/isbn9789526064437.pdf
HAL Id:hal-01592040
Audience (conference):International conference proceedings
Uncontrolled Keywords:
Institution:French research institutions > Centre National de la Recherche Scientifique - CNRS (FRANCE)
Université de Toulouse > Institut National Polytechnique de Toulouse - INPT (FRANCE)
French research institutions > Institut National de la Santé et de la Recherche Médicale - INSERM (FRANCE)
Université de Toulouse > Université Toulouse III - Paul Sabatier - UPS (FRANCE)
Université de Toulouse > Université Toulouse - Jean Jaurès - UT2J (FRANCE)
Université de Toulouse > Université Toulouse 1 Capitole - UT1 (FRANCE)
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Deposited By: IRIT IRIT
Deposited On:11 Sep 2017 13:04

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