Abstract
Support Vector Machines constitute a powerful Machine Learning technique originally designed for the solution of 2-class problems. In multiclass applications, many works divide the whole problem in multiple binary subtasks, whose results are then combined. This paper introduces a new framework for multiclass Support Vector Machines generation from binary predictors. Minimum Spanning Trees are used in the obtainment of a hierarchy of binary classifiers composing the multiclass solution. Different criteria were tested in the tree design and the results obtained evidence the efficiency of the proposed approach, which is able to produce good hierarchical multiclass solutions in polynomial time.
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Lorena, A.C., de Carvalho, A.C.P.L.F. (2005). Minimum Spanning Trees in Hierarchical Multiclass Support Vector Machines Generation. In: Ali, M., Esposito, F. (eds) Innovations in Applied Artificial Intelligence. IEA/AIE 2005. Lecture Notes in Computer Science(), vol 3533. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11504894_59
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DOI: https://doi.org/10.1007/11504894_59
Publisher Name: Springer, Berlin, Heidelberg
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