支持向量机Bootstrap方法与相关核方法的一致性
On the Consistency of the Bootstrap Approach for Support Vector Machines and Related Kernel Based Methods
University of Bayreuth（拜罗伊特大学）
摘要：
证明了基于一般凸光滑损失函数和基于一般核函数的支持向量机的自举逼近是一致的。这一结果对于用Bootstrap方法逼近未知的有限样本分布是有用的。
英文摘要：
It is shown that bootstrap approximations of support vector machines (SVMs) based on a general convex and smooth loss function and on a general kernel are consistent. This result is useful to approximate the unknown finite sample distribution of SVMs by the bootstrap approach.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
