A Gaussian process mixture prior for hearing loss modeling

M.G.H. Cox, A. de Vries

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Machine learning approaches to hearing loss estimation can significantly reduce the number of required experiments, but require a good probabilistic hearing loss model. In this work we introduce such a model, obtained by fitting a mixture of Gaussian processes to a vast database containing audiometric records of around 85k people. The learned model can be used as a prior distribution for hearing loss, and can be conditioned on age and gender. Evaluation on a test set shows that our model outperforms an optimized Gaussian process model in terms of predictive accuracy.
Original languageEnglish
Title of host publicationBenelearn 2017: Proceedings of the Twenty-Sixth Benelux Conference on Machine Learning, Technische Universiteit Eindhoven, 9-10 June 2017
EditorsW. Duivesteijn, M. Pechenizkiy, G.H.L. Fletcher
Publication statusPublished - 9 Jun 2017
EventAnnual machine learning conference of the Benelux (Benelearn 2017) - Eindhoven, Netherlands
Duration: 9 Jun 201710 Jun 2017


ConferenceAnnual machine learning conference of the Benelux (Benelearn 2017)
Abbreviated titleBenelearn 2017
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