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首页> 外文会议>International Conference on Signal Processing(ICSP'06); 20061116-20; Guilin(CN) >A New Algorithm for SVM Incremental Learning
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A New Algorithm for SVM Incremental Learning

机译：支持向量机增量学习的新算法

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摘要

Based on analyzing the relationship between the Karush-Kuhn-Tucker (KKT) conditions of support vector machine and the distribution of the training samples, the possible changes of support vector set after new samples are added to training set was analyzed, and the generalized Karush-Kuhn-Tucker conditions was defined. Based on the classification equivalence between the previous training set and the newly added training set, a new algorithm for SVM incremental learning is proposed. With the presented algorithm, the useless sample is discarded and useful information in training samples is accumulated. Experimental results with the standard datasets indicate the effectiveness of the proposed algorithm.
机译：在分析支持向量机的Karush-Kuhn-Tucker（KKT）条件与训练样本分布之间的关系的基础上，分析了将新样本添加到训练集合后支持向量集可能发生的变化，并推广了广义的Karush -库恩-塔克条件被定义。基于先前训练集与新增训练集之间的分类等价性，提出了一种新的支持向量机增量学习算法。利用所提出的算法，丢弃了无用的样本，并积累了训练样本中的有用信息。标准数据集的实验结果表明了该算法的有效性。

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