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Efficient Optimization of Multi-class Support Vector Machines with MSVMpack

Emmanuel Didiot 1 Fabien Lauer 1
1 ABC - Machine Learning and Computational Biology
LORIA - ALGO - Department of Algorithms, Computation, Image and Geometry
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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Contributor : Fabien Lauer <>
Submitted on : Tuesday, March 24, 2015 - 11:36:12 AM
Last modification on : Tuesday, December 18, 2018 - 4:18:26 PM


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  • HAL Id : hal-01134774, version 1



Emmanuel Didiot, Fabien Lauer. Efficient Optimization of Multi-class Support Vector Machines with MSVMpack. Modelling, Computation and Optimization in Information Systems and Management Sciences (MCO 2015), May 2015, Metz, France. ⟨hal-01134774⟩



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