Incremental training of support vector machines using hyperspheres
Incremental training of support vector machines using hyperspheres
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DOI：
10.1016/j.patrec.2006.02.016
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发表时间：
2006-10
期刊：
影响因子：
--
通讯作者：
Shinya Katagiri;S. Abe
中科院分区：
文献类型：
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作者：
Shinya Katagiri;S. Abe
在传统的支持向量机增量训练中，如果分离超平面随着训练数据的增加而旋转，则支持向量的候选往往被删除。针对这一问题，本文提出了一种基于单类支持向量机的增量式训练方法。首先，我们为每个类生成一个超球面。然后，我们保留超球体边界附近存在的数据作为支持向量的候选数据，并删除其他数据。通过对两类和多类基准数据集的计算机模拟，我们表明我们可以在不降低泛化能力的情况下大幅删除数据。
In the conventional incremental training of support vector machines, candidates for support vectors tend to be deleted if the separating hyperplane rotates as the training data are added. To solve this problem, in this paper, we propose an incremental training method using one-class support vector machines. First, we generate a hypersphere for each class. Then, we keep data that exist near the boundary of the hypersphere as candidates for support vectors and delete others. By computer simulations for two-class and multiclass benchmark data sets, we show that we can delete data considerably without deteriorating the generalization ability.