In order to extend common incremental learning algorithms into a parallel computation setting, an incremental learning algorithm with multiple support vector machine classifiers is proposed.
为了将一般增量学习算法扩展到并行计算环境中，提出一种基于多支持向量机分类器的增量学习算法。
An incremental training method for support vector machine is proposed to alleviate the computing burden of large-scale, high-dimension samples in multi-component gas analyzing.
针对大规模高维气体分析样本难以计算的问题，提出一种提升的支持向量机学习方法。
This algorithm, in the incremental study question, is more effective than the traditional support vector machine, with assuring the classify accuracy.
本算法在保证分类准确度的同时，在增量学习问题上比传统的支持向量机有效。
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