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Multi-task preference learning with an application to hearing aid personalization

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Title: Multi-task preference learning with an application to hearing aid personalization
Authors: Birlutiu, Adriana adrianab@cs.ru.nl, Groot, Perry1, Heskes, Tom1
Source: Neurocomputing. Mar2010, Vol. 73 Issue 7-9, p1177-1185. 9p.
Subjects: Expectation-maximization algorithms, Computer multitasking, Hearing aids, Gaussian processes, Multilevel models, Human multitasking, Auditory perception
Abstract: Abstract: We present an EM-algorithm for the problem of learning preferences with semiparametric models derived from Gaussian processes in the context of multi-task learning. We validate our approach on an audiological data set and show that predictive results for sound quality perception of hearing-impaired subjects, in the context of pairwise comparison experiments, can be improved using a hierarchical model. [Copyright &y& Elsevier]
Copyright of Neurocomputing is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 48603358
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
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  Data: Abstract: We present an EM-algorithm for the problem of learning preferences with semiparametric models derived from Gaussian processes in the context of multi-task learning. We validate our approach on an audiological data set and show that predictive results for sound quality perception of hearing-impaired subjects, in the context of pairwise comparison experiments, can be improved using a hierarchical model. [Copyright &y& Elsevier]
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  Data: <i>Copyright of Neurocomputing is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1016/j.neucom.2009.11.025
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      – SubjectFull: Hearing aids
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      – SubjectFull: Auditory perception
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              Text: Mar2010
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