Abstract
Support vector machines (SV machines, SVMs) are solved conventionally by converting the convex primal problem into a dual problem with the aid of a Lagrangian function, during whose process the non-negative Lagrangian multipliers are mandatory. Consequently, in the typical C-SVMs, the optimal solutions are given by stationary saddle points. Nonetheless, there may still exist solutions beyond the stationary saddle points. This paper explores these new points violating Karush-Kuhn-Tucker (KKT) condition.
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Liang, X. (2011). Solving Support Vector Machines beyond Dual Programming. In: Lu, BL., Zhang, L., Kwok, J. (eds) Neural Information Processing. ICONIP 2011. Lecture Notes in Computer Science, vol 7063. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24958-7_59
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DOI: https://doi.org/10.1007/978-3-642-24958-7_59
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24957-0
Online ISBN: 978-3-642-24958-7
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