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ELM Preference Learning for Physiological Data. Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. (ESANN'17)

Bacciu, Davide, Colombo, Michele, Morelli, Davide and Plans, David (2017) ELM Preference Learning for Physiological Data. Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. (ESANN'17) In: ESANN 2017, Computational Intelligence and Machine Learning, 26-28 April 2017, Bruges, Belgium.

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Abstract

The work confronts two approaches to realize preference learning using Extreme Learning Machine networks, relaying on limited and subject-dependant information concerning pairwise relations between data samples. We describe an application within the context of assessing the effect of breathing exercises on heart-rate variability, using a dataset of over 19K exercising sessions. Results highlight the importance of using weight sharing architectures to learn smooth and generalizable complete orders induced by the preference relation.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Arts and Social Sciences > Surrey Business School
Authors :
NameEmailORCID
Bacciu, Davide
Colombo, Michele
Morelli, Davided.morelli@surrey.ac.uk
Plans, Davidd.plans@surrey.ac.uk
Date : 8 November 2017
Copyright Disclaimer : Copyright 2017 The Author(s)
Related URLs :
Additional Information : Paper presented at ESANN'17 26th - 28th April 2017, Bruges, Belgium
Depositing User : Melanie Hughes
Date Deposited : 08 Aug 2017 11:39
Last Modified : 26 Jun 2018 15:54
URI: http://epubs.surrey.ac.uk/id/eprint/841855

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