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    Improving Support Vector Domain Description by Maximizing the Distance Between Negative Examples and The Minimal Sphere Center's

    Mohamed EL Boujnouni1, Mohamed Jedra2

    Computer Systems Science and Engineering, Vol.33, No.6, pp. 409-420, 2018, DOI:10.32604/csse.2018.33.409

    Abstract Support Vector Domain Description (SVDD) is an effective kernel-based method used for data description. It was motivated by the success of Support Vector Machine (SVM) and thus has inherited many of its attractive properties. It has been extensively used for novelty detection and has been applied successfully to a variety of classification problems. This classifier aims to find a sphere with minimal volume including the majority of examples that belong to the class of interest (positive) and excluding the most of examples that are either outliers or belong to other classes (negatives). In this paper More >

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