利用极大化负例与最小球面中心之间的距离改进支持向量域描述

Mohamed el Boujnouni, M. Jedra
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引用次数: 1

摘要

支持向量域描述(SVDD)是一种有效的基于核的数据描述方法。它是由支持向量机(SVM)的成功所驱动的,因此继承了支持向量机的许多吸引人的特性。它已被广泛用于新颖性检测,并已成功地应用于各种分类问题。这个分类器的目标是找到一个体积最小的球体,其中包括属于感兴趣类别的大多数示例(正),并排除大多数异常值或属于其他类别的示例(负)。本文提出了一种提高SVDD分类精度的新方法。这一目标将通过利用训练步骤中存在的负例来实现,而不会增加解决该分类器的二次规划问题所需的计算时间和内存资源。在棋盘和两个螺旋这两个具有挑战性的人工问题以及4个基准数据集上的仿真结果验证了该方法的有效性。
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Improving Support Vector Domain Description by Maximizing the Distance Between Negative Examples and The Minimal Sphere Center's
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 we propose a new approach to improve the classification accuracy of SVDD. This objective will be achieved by exploiting the existence of negative examples in the training step, without increasing the computational time and memory resources required to solve the quadratic programming problem of that classifier. Simulation results on two challenging artificial problems, namely chessboard and two spirals, and four benchmark datasets have successfully validated the effectiveness of the proposed method.
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