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Multiple kernel learning algorithms and their use in biomedical informatics

Tripoliti Evanthia Eleftherios, Zervakis Michail, Fotiadis, Dimitrios Ioannou

Απλή Εγγραφή


URIhttp://purl.tuc.gr/dl/dias/93973880-D0BD-4269-B757-17064C1DEA08-
Αναγνωριστικόhttps://link.springer.com/chapter/10.1007/978-3-319-32703-7_109-
Αναγνωριστικόhttps://doi.org/10.1007/978-3-319-32703-7_109-
Γλώσσαen-
Μέγεθος6 pagesen
ΤίτλοςMultiple kernel learning algorithms and their use in biomedical informaticsen
ΔημιουργόςTripoliti Evanthia Eleftheriosen
ΔημιουργόςZervakis Michailen
ΔημιουργόςΖερβακης Μιχαηλel
ΔημιουργόςFotiadis, Dimitrios Ioannouen
ΕκδότηςSpringer Verlagen
ΠερίληψηMultiple 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
ΤύποςΠλήρης Δημοσίευση σε Συνέδριοel
ΤύποςConference Full Paperen
Άδεια Χρήσηςhttp://creativecommons.org/licenses/by/4.0/en
Ημερομηνία2018-11-22-
Ημερομηνία Δημοσίευσης2016-
Θεματική ΚατηγορίαKernel methodsen
Θεματική ΚατηγορίαMultiple kernel learningen
Βιβλιογραφική ΑναφοράE. 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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