ELM Preference Learning for Physiological Data

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

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

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