Online Boosting Algorithm Based on Two-Phase SVM Training
Online Boosting Algorithm Based on Two-Phase SVM Training
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DOI：
10.5402/2012/740761
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发表时间：
2012-08
期刊：
影响因子：
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通讯作者：
V. Yugov;I. Kumazawa
中科院分区：
文献类型：
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作者：
V. Yugov;I. Kumazawa
我们描述和分析了一个简单有效的两步在线boosting算法，该算法使我们能够利用高效的基于梯度下降的在线SVM训练方法，而无需微调内核参数，我们通过几个实验证明了其效率。我们的方法类似于AdaBoost，因为它根据先前训练的分类器提供的权重训练额外的分类器，但与AdaBoost不同的是，我们利用铰链损失而不是指数损失，并修改在线设置的算法，允许不同数量的分类器。我们表明，我们的理论收敛界是类似的早期算法，同时允许更大的灵活性。我们的方法也可以很容易地将额外的非线性形式的美世内核，虽然我们的实验表明，这是不必要的，大多数情况下。在我们的算法中，额外分类器的预训练允许更高的准确性，同时减少与通常基于内核的方法相关的时间。我们比较我们的算法与其他在线训练算法，我们表明，对于大多数情况下，未知的内核参数，我们的算法优于其他算法在运行时间和收敛速度。
We describe and analyze a simple and effective two-step online boosting algorithm that allows us to utilize highly effective gradient descent-based methods developed for online SVM training without the need to fine-tune the kernel parameters, and we show its efficiency by several experiments. Our method is similar to AdaBoost in that it trains additional classifiers according to the weights provided by previously trained classifiers, but unlike AdaBoost, we utilize hinge-loss rather than exponential loss and modify algorithm for the online setting, allowing for varying number of classifiers. We show that our theoretical convergence bounds are similar to those of earlier algorithms, while allowing for greater flexibility. Our approach may also easily incorporate additional nonlinearity in form of Mercer kernels, although our experiments show that this is not necessary for most situations. The pre-training of the additional classifiers in our algorithms allows for greater accuracy while reducing the times associated with usual kernel-based approaches. We compare our algorithm to other online training algorithms, and we show, that for most cases with unknown kernel parameters, our algorithm outperforms other algorithms both in runtime and convergence speed.