Learning with progressive transductive support vector machine
Learning with progressive transductive support vector machine
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DOI：
10.1016/s0167-8655(03)00008-4
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发表时间：
2003-08-01
影响因子：
5.1
通讯作者：
Dong, SH
中科院分区：
文献类型：
--
作者：
Chen, YS;Wang, GP;Dong, SH
支持向量机是近年来在统计学习理论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直推方法而不是归纳方法，工作集可以用作关于边缘的附加信息源。与传统的归纳支持向量机相比，直推支持向量机具有更强的性能。在转换中，使用来自训练和工作集数据的信息来估计工作集中的点处的分类函数。这将有助于提高支持向量机的泛化性能，特别是当训练数据不足时。直观地说，当训练集很小时，或者当总人口的训练集和工作集子样本之间存在显著偏差时，我们会期望转导学习产生改进。本文提出了一种渐进式直推支持向量机，以扩展Joachims的直推支持向量机来处理不同的类分布。它解决了必须从工作集中估计阳性/阴性示例的比率的问题。实验结果表明，该算法具有很好的应用前景。(C)2003 Elsevier Science B. V.保留所有权利。
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 working set can be used as an additional source of information about margins. Compared with traditional inductive support vector machines, transductive support vector machine is often more powerful and can give better performance. In transduction, one estimates the classification function at points within the working set using information from both the training and the working set data. This will help to improve the generalization performance of SVMs, especially when training data is inadequate. 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 the algorithm is very promising. (C) 2003 Elsevier Science B.V. All rights reserved.