Support Kernel Machine-Based Active Learning to Find Labels and a Proper Kernel Simultaneously-猫眼课题宝

DOI：

10.1007/978-3-540-77226-2_29

发表时间：

2007-12

期刊：

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中科院分区：

其他

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作者：

Y. Sinohara;A. Takasu

基于支持向量机的主动学习已经成功地应用于有大量未标记样本但获得其标记代价较高的情况。然而，在主动学习过程之前，需要对支持向量机中使用的核进行适当的修正。如果预先选择的核函数不适合目标数据，则学习的支持向量机性能较差。因此，需要新的主动学习方法来有效地为目标数据和未知样本的标签找到合适的核。为此，提出了一种基于SKM的两阶段主动学习方法和采样策略。实验表明，基于SKM的主动学习方法响应速度快，适合与人类专家交互，并能在给定的多个核函数的线性组合中找到合适的核函数。我们还证明了在所提出的抽样策略下，它比流行的抽样策略差值更早地收敛到适当的核组合。

SVM-based active learning has been successfully applied when a large number of unlabeled samples are available but getting their labels is costly. However, the kernel used in SVM should be fixed properly before the active learning process. If the pre-selected kernel is inadequate for the target data, the learned SVM has poor performance. So, new active learning methods are required which effectively find an adequate kernel for the target data as well as the labels of unknown samples.In this paper, we propose a two-phased SKM-based active learning method and a sampling strategy for the purpose. By experiments, we show that the proposed SKM-based active learning method has quick response suited to interaction with human experts and can find an appropriate kernel among linear combinations of given multiple kernels. We also show that with the proposed sampling strategy, it converges earlier to the proper combination of kernels than with the popular sampling strategy MARGIN.

Support Kernel Machine-Based Active Learning to Find Labels and a Proper Kernel Simultaneously

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