Sphere Support Vector Machines for large classification tasks

Robert Strack; Vojislav Kecman; Beata Strack; Qi Li

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Sphere Support Vector Machines for large classification tasks

机译：适用于大型分类任务的Sphere Support Vector Machines

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摘要

This paper introduces Sphere Support Vector Machines (SVMs) as the new fast classification algorithm based on combining a minimal enclosing ball approach, state of the art nearest point problem solvers and probabilistic techniques. The blending of the three significantly speeds up the training phase of SVMs and also attains practically the same accuracy as the other classification models over several large real datasets within the strict validation frame of a double (nested) cross-validation. The results shown are promoting SphereSVM as outstanding alternatives for handling large and ultra-large datasets in a reasonable time without switching to various parallelization schemes for SVM algorithms recently proposed.

机译：本文将球支持向量机（SVM）作为一种新的快速分类算法，基于最小包围球方法，最新的点问题求解器和概率技术相结合。在双重（嵌套）交叉验证的严格验证框架内，这三种方法的融合显着加快了SVM的训练阶段，并且在几个大型真实数据集上，几乎达到了与其他分类模型相同的准确性。显示的结果使SphereSVM成为在合理时间内处理大型和超大型数据集的出色替代方案，而无需切换到最近提出的SVM算法的各种并行化方案。

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来源
《Neurocomputing》 |2013年第4期|59-67|共9页
作者
Robert Strack; Vojislav Kecman; Beata Strack; Qi Li;
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作者单位

Virginia Commonwealth University, Computer Science Department, Richmond, VA 23284-3019, United States;

Virginia Commonwealth University, Computer Science Department, Richmond, VA 23284-3019, United States;

Virginia Commonwealth University, Computer Science Department, Richmond, VA 23284-3019, United States;

Virginia Commonwealth University, Computer Science Department, Richmond, VA 23284-3019, United States;

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收录信息美国《科学引文索引》(SCI);美国《工程索引》(EI);
原文格式 PDF
正文语种 eng
中图分类
关键词
support vector machines; core vector machines; minimum enclosing ball; large datasets; classification;

机译：支持向量机;核心向量机;最小围球大型数据集;分类;

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3. Maximum Margin of Twin Spheres Support Vector Machine for Imbalanced Data Classification [J] . Yitian Xu Cybernetics, IEEE Transactions on . 2017,第6期

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7. Piecewise Combination of Hyper-Sphere Support Vector Machine for Multi-Class Classification Problems [O] . Shuang Liu 2019

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8. Fast query-optimized kernel machine classification via incremental approximate nearest support vectors [R] . DeCoste, D., Mazzoni, D. 2003

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Sphere Support Vector Machines for large classification tasks

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