改进的密度诱导支持向量数据描述

F. Yin, Guang-Xin Huang
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引用次数: 2

摘要

支持向量数据描述(SVDD)是一种能够对目标数据集进行球形描述的数据描述方法。本文提出了一种密度诱导SVDD (D-SVDD)来改进SVDD。然而,D-SVDD的对偶优化问题并不是一个简单的优化问题,这使得D-SVDD不是一种简单的数据描述方法。本文提出了一种改进的密度诱导SVDD。该方法的超球面边界涉及到一个众所周知的二次规划问题,因此所提出的数据描述方法改进了D-SVDD。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Improved density-induced support vector data description
Support vector data description (SVDD) is a data description method which can give the target data set a spherically shaped description. A density-induced SVDD (D-SVDD) has been proposed to improve the SVDD. However, the dual optimization problem of the D-SVDD is not a simple optimization problem which makes the D-SVDD be not an easy data description method. This paper presents an improved density-induced SVDD. The hyper-spherically shaped boundary of our method resorts to a well-known quadratic programming problem, thus the proposed data description method improves the D-SVDD.
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