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。

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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。

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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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