Improving support vector data description using local density degree
Improving support vector data description using local density degree
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DOI：
10.1016/j.patcog.2005.03.020
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发表时间：
2005-10-01
影响因子：
8
通讯作者：
Lee, KH
中科院分区：
文献类型：
--
作者：
Lee, K;Kim, DW;Lee, KH
我们提出了一种新的支持向量数据描述（SVDD），通过为每个数据点引入局部密度度，将训练数据集的局部密度结合起来。通过使用基于度的密度诱导距离度量，我们重新制定了传统的SVDD。在不同的真实的数据集上的实验表明，该方法比传统的SVDD更准确地描述了训练数据集。(c)2005模式识别学会。由爱思唯尔有限公司出版。保留所有权利。
We propose a new support vector data description (SVDD) incorporating the local density of a training data set by introducing a local density degree for each data point. By using a density-induced distance measure based on the degree, we reformulate a conventional SVDD. Experiments with various real data sets show that the proposed method more accurately describes training data sets than the conventional SVDD in all tested cases. (c) 2005 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.