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首页> 外文期刊>Pattern Analysis and Machine Intelligence, IEEE Transactions on >Nonconvex Online Support Vector Machines
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Nonconvex Online Support Vector Machines

机译：非凸在线支持向量机

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摘要

In this paper, we propose a nonconvex online Support Vector Machine (SVM) algorithm (LASVM-NC) based on the Ramp Loss, which has the strong ability of suppressing the influence of outliers. Then, again in the online learning setting, we propose an outlier filtering mechanism (LASVM-I) based on approximating nonconvex behavior in convex optimization. These two algorithms are built upon another novel SVM algorithm (LASVM-G) that is capable of generating accurate intermediate models in its iterative steps by leveraging the duality gap. We present experimental results that demonstrate the merit of our frameworks in achieving significant robustness to outliers in noisy data classification where mislabeled training instances are in abundance. Experimental evaluation shows that the proposed approaches yield a more scalable online SVM algorithm with sparser models and less computational running time, both in the training and recognition phases, without sacrificing generalization performance. We also point out the relation between nonconvex optimization and min-margin active learning.
机译：本文提出了一种基于斜坡损失的非凸在线支持向量机算法（LASVM-NC），具有较强的抑制离群值影响的能力。然后，再次在在线学习环境中，我们提出了一种基于凸优化中近似非凸行为的离群滤波机制（LASVM-I）。这两种算法建立在另一种新颖的SVM算法（LASVM-G）的基础上，该算法能够利用对偶间隙在迭代步骤中生成准确的中间模型。我们提供的实验结果证明了我们的框架在对噪声数据分类中的异常值实现显着鲁棒性方面的优势，在这些数据中，错误标记的训练实例很多。实验评估表明，所提出的方法在训练和识别阶段都具有稀疏模型和更少的计算运行时间，并且具有可扩展性更高的在线SVM算法，而不会影响泛化性能。我们还指出了非凸优化和最小余量主动学习之间的关系。

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