“VINE”结构的多类分类

Somjet Suppharangsan, M. Niranjan
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引用次数: 0

摘要

在本文中,我们提出了一种新的基于One-Versus-All或ova的多类分类方案,旨在减少支持向量机(svm)的训练时间,特别是在大型数据集上。在10个基准数据集上的实验结果表明,本文提出的“VINE”方案的性能与之前的OVA方案相当,但前者的训练时间比后者少。对于具有大量维度和实例的问题,可以将VINE和特征选择相结合以获得进一步的加速。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multi-class Classification on "VINE" Structure
In this paper we present a new One-Versus-All or OVA-based scheme for multi-class classification problems, aiming to reduce the training time when applying support vector machines (SVMs), particularly on large datasets. The experimental results on ten benchmark datasets show that the performance of the proposed scheme, referred to as "VINE", is comparable to that of its predecessor OVA scheme, but the former spends less training time than the latter scheme. On the problems with a large number of dimensions and instances, it is possible to combine VINE and a feature selection to obtain further speedup.
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