计算机科学 ›› 2016, Vol. 43 ›› Issue (2): 230-234.doi: 10.11896/j.issn.1002-137X.2016.02.048
郝运河,张浩峰
HAO Yun-he and ZHANG Hao-feng
摘要： 提出了一种基于双支持向量回归机的增量学习算法。将获取到的新样本加入训练数据集后,该算法无需在整个新的数据集上重新训练双支持向量回归机,而是充分利用增量前的计算信息,从而大大减少了模型更新中逆矩阵的计算量,提高了算法的执行效率。在人工数据集、时间序列预测和UCI数据集上的数值实验表明,该算法快速有效。
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