Abstract
In the field of machine learning, multi-class support vector machines (M-SVMs) are state-of-the-art classifiers with training algorithms that amount to convex quadratic programs. However, solving these quadratic programs in practice is a complex task that typically cannot be assigned to a general purpose solver. The paper describes the main features of an efficient solver for M-SVMs, as implemented in the MSVMpack software. The latest additions to this software are also highlighted and a few numerical experiments are presented to assess its efficiency.
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Didiot, E., Lauer, F. (2015). Efficient Optimization of Multi-class Support Vector Machines with MSVMpack. In: Le Thi, H., Pham Dinh, T., Nguyen, N. (eds) Modelling, Computation and Optimization in Information Systems and Management Sciences. Advances in Intelligent Systems and Computing, vol 360. Springer, Cham. https://doi.org/10.1007/978-3-319-18167-7_3
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DOI: https://doi.org/10.1007/978-3-319-18167-7_3
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-18166-0
Online ISBN: 978-3-319-18167-7
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