Learning with progressive transductive Support Vector Machine
Learning with progressive transductive Support Vector Machine
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DOI：
10.1109/icdm.2002.1183887
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发表时间：
2002-12
期刊：
影响因子：
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通讯作者：
Yisong Chen;Guoping Wang;Shihai Dong
中科院分区：
文献类型：
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作者：
Yisong Chen;Guoping Wang;Shihai Dong
支持向量机（SVM）是近年来在统计学习理论的基础上发展起来的一种新的学习方法。通过在支持向量分类器中采用转换方法而不是归纳方法，测试集可以用作关于边缘的额外信息来源。直观地说，当训练集很小，或者当总体的训练集和工作集子样本之间存在显著偏差时，我们会期望转换学习产生改进。本文提出了一种渐进式换能化支持向量机，将Joachims的换能化支持向量机扩展到处理不同的类分布。它解决了必须从工作集中估计正/负示例的比例的问题。实验结果表明，该算法是很有前途的。
Support Vector Machine (SVM) is a new learning method developed in recent years based on the foundations of statistical learning theory. By taking a transductive approach instead of an inductive one in support vector classifiers, the test set can be used as an additional source of information about margins. Intuitively, we would expect transductive learning to yield improvements when the training sets are small or when there is a significant deviation between the training and working set subsamples of the total population. In this paper, a progressive transductive support vector machine is addressed to extend Joachims' Transductive SVM to handle different class distributions. It solves the problem of having to estimate the ratio of positive/negative examples from the working set. The experimental results show that the algorithm is very promising.