Svm Classifiers with Moderated Outputs for Automatic Classification in Molecular Biology
Date Issued
2002
Author(s)
Madevska Bogdanova, Ana
Nikolikj, D
Abstract
We present an alternative way of interpreting and modifying the
outputs of the Support Vector Machine (SVM) classifiers – method MSVMO
(Modified SVM Outputs). Stemming from the geometrical interpretation of
the SVM outputs as a distance of individual patterns from the hyperplane, allows us to calculate its posterior probability i.e. to construct a probabilitybased measure of belonging to one of the classes, depending on the vector’s
relative distance from the hyperplane.
We illustrate the results by providing suitable analysis of three classification
problems and comparing them with an already published method for modifying SVM outputs.
outputs of the Support Vector Machine (SVM) classifiers – method MSVMO
(Modified SVM Outputs). Stemming from the geometrical interpretation of
the SVM outputs as a distance of individual patterns from the hyperplane, allows us to calculate its posterior probability i.e. to construct a probabilitybased measure of belonging to one of the classes, depending on the vector’s
relative distance from the hyperplane.
We illustrate the results by providing suitable analysis of three classification
problems and comparing them with an already published method for modifying SVM outputs.
Subjects
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