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Multiple kernel learning algorithms and their use in biomedical informatics

Tripoliti Evanthia Eleftherios, Zervakis Michail, Fotiadis, Dimitrios Ioannou

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URIhttp://purl.tuc.gr/dl/dias/93973880-D0BD-4269-B757-17064C1DEA08-
Identifierhttps://link.springer.com/chapter/10.1007/978-3-319-32703-7_109-
Identifierhttps://doi.org/10.1007/978-3-319-32703-7_109-
Languageen-
Extent6 pagesen
TitleMultiple kernel learning algorithms and their use in biomedical informaticsen
CreatorTripoliti Evanthia Eleftheriosen
CreatorZervakis Michailen
CreatorΖερβακης Μιχαηλel
CreatorFotiadis, Dimitrios Ioannouen
PublisherSpringer Verlagen
Content SummaryMultiple kernel learning (MKL) is a parametric kernel learning approach which allows the combination of multiple kernels for a given learning task. Studies reported in the literature have demonstrated the potentiality of MKL algorithms to address a wide range of machine learning tasks and especially biomedical applications. The aim of this paper is to present a review of MKL algorithms in order classification, feature selection and feature fusion problems to be addressed. Through the review the following issues are presented: a) the key properties of the MKL algorithms, b) how the MKL algorithms address issues regarding the nature of the datasets (missing data, multi classes, categorical features etc.), and c) the selection of kernels.en
Type of ItemΠλήρης Δημοσίευση σε Συνέδριοel
Type of ItemConference Full Paperen
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2018-11-22-
Date of Publication2016-
SubjectKernel methodsen
SubjectMultiple kernel learningen
Bibliographic CitationE. E. Tripoliti, M. Zervakis and D. I. Fotiadis, "Multiple kernel learning algorithms and their use in biomedical informatics," in 14th Mediterranean Conference on Medical and Biological Engineering and Computing, 2016, pp. 553-558. doi: 10.1007/978-3-319-32703-7_109en

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