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The training of Support Vector Machines may be a very difficult task whendealing with very large datasets. The memory requirement and the timeconsumption of the SVMs algorithms grow rapidly with the increase of the data.To overcome these drawbacks, we propose a parallel decomposition algorithmicscheme for SVMs training for which we prove global convergence under suitableconditions. We outline how these assumptions can be satisfied in practice andwe suggest various specific implementations exploiting the adaptable structureof the algorithmic model.

A Class of Parallel Decomposition Algorithms for SVMs Training