Multiple kernel learning algorithms and their use in biomedical informatics

Summarization: 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.Παρουσιάστηκε στο: 14th Mediterranean Conference on Medical and Biological Engineering and Computin