Institutional Repository
Technical University of Crete
Technical University of Crete
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| URI | http://purl.tuc.gr/dl/dias/93973880-D0BD-4269-B757-17064C1DEA08 | - |
| Identifier | https://link.springer.com/chapter/10.1007/978-3-319-32703-7_109 | - |
| Identifier | https://doi.org/10.1007/978-3-319-32703-7_109 | - |
| Language | en | - |
| Extent | 6 pages | en |
| Title | Multiple kernel learning algorithms and their use in biomedical informatics | en |
| Creator | Tripoliti Evanthia Eleftherios | en |
| Creator | Zervakis Michail | en |
| Creator | Ζερβακης Μιχαηλ | el |
| Creator | Fotiadis, Dimitrios Ioannou | en |
| Publisher | Springer Verlag | en |
| Content Summary | Multiple 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 Item | Πλήρης Δημοσίευση σε Συνέδριο | el |
| Type of Item | Conference Full Paper | en |
| License | http://creativecommons.org/licenses/by/4.0/ | en |
| Date of Item | 2018-11-22 | - |
| Date of Publication | 2016 | - |
| Subject | Kernel methods | en |
| Subject | Multiple kernel learning | en |
| Bibliographic Citation | E. 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_109 | en |
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