Multiple Kernel Learning Methods and their Application in Yeast Protein Subcellular Localization Prediction

Spirovska, Kristina and Madevska-Bogdanova, Ana (2012) Multiple Kernel Learning Methods and their Application in Yeast Protein Subcellular Localization Prediction. In: Proceedings of the Nineth Conference on Informatics and Information Technology. Faculty of Computer Science and Engineering, Ss. Cyril and Methodius University in Skopje, Macedonia, Skopje, Macedonia, pp. 101-104. ISBN 978-608-4699-01-9

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Abstract

Kernel methods are becoming more and more popular technique for solving machine learning problems. Recent advances in the field of Multiple Kernel Learning (MKL) have highlighted MKL as an attractive tool that can be applied in many supervised learning tasks. During the past decade, it has been shown that classifiers that use combinations of multiple kernels instead of classical single kernel-based ones attain significantly better results in certain problems. We give an overview of the existing multiple kernel learning methods and present experimental results of their application in the bioinformatics domain.

Item Type: Book Section
Uncontrolled Keywords: Multiple Kernel Learning (MKL), kernel methods, kernel function, SVM
Subjects: International Conference on Informatics and Information Technologies > Intelligent Systems
International Conference on Informatics and Information Technologies > Robotics
International Conference on Informatics and Information Technologies > Bioinformatics
Depositing User: Vangel Ajanovski
Date Deposited: 28 Oct 2016 00:15
Last Modified: 28 Oct 2016 00:15
URI: http://eprints.finki.ukim.mk/id/eprint/11160

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