Face verification with a kernel fusion method
Pattern Recognition Letters
Multiple Kernel Learning Algorithms
The Journal of Machine Learning Research
Section-Wise similarities for classification of subjective-data on time series
CIARP'11 Proceedings of the 16th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Review: Supervised classification and mathematical optimization
Computers and Operations Research
Functional analysis techniques to improve similarity matrices in discrimination problems
Journal of Multivariate Analysis
A nested heuristic for parameter tuning in Support Vector Machines
Computers and Operations Research
Asymmetric clustering using the alpha-beta divergence
Pattern Recognition
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The problem of combining different sources of information arises in several situations, for instance, the classification of data with asymmetric similarity matrices or the construction of an optimal classifier from a collection of kernels. Often, each source of information can be expressed as a similarity matrix. In this paper we propose a new class of methods in order to produce, for classification purposes, a single kernel matrix from a collection of kernel (similarity) matrices. Then, the constructed kernel matrix is used to train a Support Vector Machine (SVM). The key ideas within the kernel construction are twofold: the quantification, relative to the classification labels, of the difference of information among the similarities; and the extension of the concept of linear combination of similarity matrices to the concept of functional combination of similarity matrices. The proposed methods have been successfully evaluated and compared with other powerful classifiers and kernel combination techniques on a variety of artificial and real classification problems.