基于支持向量机的简单快速多实例分类

2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology Pub Date : 2012-12-04 DOI:10.1109/WI-IAT.2012.50

Zhiquan Qi, Ying-jie Tian, Yong Shi

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引用次数: 1

摘要

本文提出了一种基于支持向量机的简单快速的多实例分类方法。与其他传统的多实例学习方法相比，我们的方法只需要求解一个二次规划问题就可以处理多实例学习问题。因此Fast MI-SVM的训练时间非常快。在基准数据集上的数值实验表明了该方法的可行性和有效性。

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A Simple and Fast Multi-instance Classification via Support Vector Machine

In this paper, we proposed a Simple and Fast Multi-Instance Classification Via Support Vector Machine(called Fast MI-SVM). Compared with the other conventional Multi-Instance learning method, our method is able to deal with multi-instance learning problem by only solving a quadratic programming problem. So the training time of Fast MI-SVM is very fast. All numerical experiments on benchmark datasets show the feasibility and validity of the proposed method.

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来源期刊

2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology

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