基于Cache的渐进直推式支持向量机学习算法
首发时间:2009-04-01
摘要:支持向量机(support vector machine)是近年来在统计学习理论的基础上发展起来的一种新的模式识别方法,在解决小样本、非线性及高维模式识别问题中表现出许多特有的优势。直推式学习(transductive inference)试图根据已知样本对特定的未知样本建立一套进行识别的方法和准则。渐进直推式支持向量机学习算法(progressive transductive support vector machine , PTSVM )可以较好地适应各种不同的训练样本分布,实现了较一般意义上的直推式学习。本文针对PTSVM中的标签重置法纠错能力不强的缺陷,提出了一种有效的基于Cache的渐进直推式支持向量机学习算法。该算法大大减少了错误标记的次数,提高了算法的速度和准确度。实验数据表明该算法是有效的。
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An Cache-based PTSVM Learning Algorithm
Abstract:Support vector machine is a new learning method developed in recent years based on the foundations of statistical learning theory. It is gaining popularity due to many attractive features and promising empirical performance in the fields of nonlinear and high dimensional pattern recognition. TSVM (transductive support vector machine) takes into account a particular test set and tries to minimize misclassifications of just those particular examples. PTSVM (progressive transductive support vector machine) can automatically adapt to different data distributions and realize a transductive learning of support vectors in a more general sense. Although dynamical adjusting offers some sort of error recovery function, its ability is limited. In allusion to the shortcoming of dynamical adjusting of PTSVM learning algorithm, CPTSVM (an effective cache-based PTSVM) learning algorithm is presented. The algorithm greatly reduces the number of mis-labeling and improves the speed and accuracy. Experiments data show the validity of this algorithm.
Keywords: statistical learning theory support vector machine transductive inference cache
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No.3097345515912385****
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