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The use of binary support vector machines (SVMs) in multi-classification is addressed in this paper. Margins
associated to the bi-classifiers, since they depend on the geometrical disposition of the classes being
separated, are, in general, of various magnitudes. In order to overcome this scaling problem, a normalization
process should be applied on the SVMs’ outputs. Thus, a new normalization approach is presented
based on the convex hulls that contain the classes to be separated. Furthermore, a theoretical study is
developed which justifies the proposed approach, and an interpretation is provided. An empirical study
is also carried out to compare this normalization with others found in the literature.
CitationGonzález, L. [et al.]. A study on output normalization in multiclass SVMs. "Pattern recognition letters", 01 Febrer 2013, vol. 34, núm. 3, p. 344-348.
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