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Least squares support vector machine (LS-SVM) classifiers have been traditionally trained with conjugate gradient algorithms. In this work, completing the study by Keerthi et al., we explore the applicability of the SMO algorithm for solving the LS-SVM problem, by comparing First Order and Second Order working set selections concentrating on the RBF kernel, which is the most usual choice in practice. It turns out that, considering all the range of possible values of the hyperparameters, Second Order working set selection is altogether more convenient than First Order. In any case, whichever the selection scheme is, the number of kernel operations performed by SMO appears to scale quadratically with the number of patterns. Moreover, asymptotic convergence to the optimum is proved and the rate of convergence is shown to be linear for both selections.

Abstract. Let cr(Kn) be the minimum number of crossings over all rectilinear drawings of the complete graph on n vertices on the plane. In this paper we prove that cr(Kn) < 0.380473 n 4 + Θ(n 3 ); improving thus on the previous best known upper bound. This is done by obtaining new rectilinear drawings of Kn for small values of n, and then using known constructions to obtain arbitrarily large good drawings from smaller ones. The "small" sets where found using a simple heuristic detailed in this paper.

Abstract. SVM training is usually discussed under two different algorithmic points of view. The first one is provided by decomposition methods such as SMO and SVMLight while the second one encompasses geometric methods that try to solve a Nearest Point Problem (NPP), the GilbertSchlesinger-Kozinec (GSK) and Mitchell-Demyanov-Malozemov (MDM) algorithms being the most representative ones. In this work we will show that, indeed, both approaches are essentially coincident. More precisely, we will show that a slight modification of SMO in which at each iteration both updating multipliers correspond to patterns in the same class solves NPP and, moreover, that this modification coincides with an extended MDM algorithm. Besides this, we also propose a new way to apply the MDM algorithm for NPP problems over reduced convex hulls.