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首页> 外文期刊>Journal of computational and graphical statistics: A joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America >Multiclass Probability Estimation With Support Vector Machines
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Multiclass Probability Estimation With Support Vector Machines

机译：具有支持向量机的多级概率估计

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摘要

Multiclass classification and probability estimation have important applications in data analytics. Support vector machines (SVMs) have shown great success in various real-world problems due to their high classification accuracy. However, one main limitation of standard SVMs is that they do not provide class probability estimates, and thus fail to offer uncertainty measure about class prediction. In this article, we propose a simple yet effective framework to endow kernel SVMs with the feature of multiclass probability estimation. The new probability estimator does not rely on any parametric assumption on the data distribution, therefore, it is flexible and robust. Theoretically, we show that the proposed estimator is asymptotically consistent. Computationally, the new procedure can be conveniently implemented using standard SVM softwares. Our extensive numerical studies demonstrate competitive performance of the new estimator when compared with existing methods such as multiple logistic regression, linear discrimination analysis, tree-based methods, and random forest, under various classification settings. for this article are available online.
机译：多字符分类和概率估计在数据分析中具有重要应用。由于其高分类准确性，支持向量机（SVM）在各种现实问题上表现出巨大的成功。然而，标准SVM的一个主要限制是它们不提供类概率估计，因此未能提供关于类预测的不确定性测量。在本文中，我们提出了一个简单但有效的框架，以赋予内核SVM与多级概率估计的特征。新的概率估计器不依赖于数据分布上的任何参数假设，因此，它是灵活且鲁棒的。从理论上讲，我们表明所提出的估计者是渐近的。计算地，可以使用标准SVM软件方便地实现新的过程。我们广泛的数值研究表明，与多种逻辑回归，线性歧视分析，树的方法和随机林等现有方法相比，新估算器的竞争性能在各种分类设置下。本文可在线获取。

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