Abstract
Building upon Gilbert’s convergence proof of his algorithtm to solve the Minimum Norm Problem, we establish a framework where a much simplified version of his proof allows us to prove the convergence of two algorithms for solving the Nearest Point Problem for disjoint convex hulls, namely the GSK and the MDM algorithms, as well as the convergence of the SMO algorithm for SVMs over linearly separable two–class samples.
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References
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López, J., Dorronsoro, J.R. (2010). A Common Framework for the Convergence of the GSK, MDM and SMO Algorithms. In: Diamantaras, K., Duch, W., Iliadis, L.S. (eds) Artificial Neural Networks – ICANN 2010. ICANN 2010. Lecture Notes in Computer Science, vol 6353. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15822-3_10
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DOI: https://doi.org/10.1007/978-3-642-15822-3_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15821-6
Online ISBN: 978-3-642-15822-3
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