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Abstract: Kernel-based learning algorithms work by embedding the data into a Euclidean space, and then searching for linear relations among the embedded data points. The embedding is performed implicitly, by specifying the inner products between each pair of points in the embedding space. This information is contained in the so-called kernel matrix, a symmetric and positive definite matrix that encodes the relative positions of all points. Specifying this matrix amounts to specifying the geometry of the... (Update)
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BibTeX entry: (Update)
G. R. G. Lanckriet, P. Bartlett, N. Cristianini, L. El Ghaoui, and M. I. Jordan. Learning the kernel matrix with semidefinite programming. In International Conf. on Machine Learning, 2002. http://citeseer.ist.psu.edu/lanckriet02learning.html More
@misc{ gert-learning,
author = "Gert Lanckriet, Nello Cristianini, Peter Bartlett, Laurent El Ghaoui, Michael Jordan",
title = "Learning the Kernel Matrix with Semi-Definite Programming",
url = "citeseer.ist.psu.edu/lanckriet02learning.html" }
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