Multiple Incremental Decremental Learning of Support Vector Machines
Multiple Incremental Decremental Learning of Support Vector Machines
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DOI：
10.1109/tnn.2010.2048039
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发表时间：
2010-07-01
影响因子：
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通讯作者：
Takeuchi, Ichiro
中科院分区：
文献类型：
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作者：
Karasuyama, Masayuki;Takeuchi, Ichiro
提出了一种支持向量机（SVM）的多重增量减量算法。在在线学习中，当一些新的观察到达和/或一些观察变得过时时，我们需要更新训练模型。如果需要增加或删除单个数据点，可以采用传统的单点增量/减量算法来有效地更新模型。然而，为了添加和/或删除多个数据点，当前更新算法的计算成本变得过高，因为我们需要对每个数据点重复应用它。在本文中，我们开发了一个扩展的增量递减算法，有效地适用于多个数据点的同时更新。理论分析和实验结果表明，该算法能够有效地降低计算量.我们的方法对于在线SVM学习特别有用，因为我们需要在短时间内删除旧数据点并添加新数据点。
We propose a multiple incremental decremental algorithm of support vector machines (SVM). In online learning, we need to update the trained model when some new observations arrive and/or some observations become obsolete. If we want to add or remove single data point, conventional single incremental decremental algorithm can be used to update the model efficiently. However, to add and/or remove multiple data points, the computational cost of current update algorithm becomes inhibitive because we need to repeatedly apply it for each data point. In this paper, we develop an extension of incremental decremental algorithm which efficiently works for simultaneous update of multiple data points. Some analyses and experimental results show that the proposed algorithm can substantially reduce the computational cost. Our approach is especially useful for online SVM learning in which we need to remove old data points and add new data points in a short amount of time.