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普遍性的关系,特征的内核和算法嵌入的措施

On the relation between universality, characteristic kernels and RKHS embedding of measures
课程网址: http://videolectures.net/aistats2010_sriperumbudur_otrbu/  
主讲教师: Bharath K. Sriperumbudur
开课单位: 圣地亚哥大学
开课时间: 信息不详。欢迎您在右侧留言补充。
课程语种: 英语
中文简介:
通用内核已被证明中扮演重要角色的成就感通过许多基于算法贝叶斯风险,包括二进制分类、回归,等。在这篇文章中,我们提出一个普遍性的概念,概括了Steinwart引入的概念和Micchelli等人研究一个内核的充分必要条件是普遍的。我们证明了所有这些通用性的概念都与一类波莱尔测度的单射嵌入到一个可再生核希尔伯特空间(RKHS)密切相关。利用普适性与波列测度嵌入RKHS之间的关系,建立了普适性与特征核之间的关系。后者是在概率测度的RKHS嵌入的背景下提出的,用于同质性检验、独立性检验等统计应用。
课程简介: Universal kernels have been shown to play an important role in the achievability of the Bayes risk by many kernel-based algorithms that include binary classification, regression, etc. In this paper, we propose a notion of universality that generalizes the notions introduced by Steinwart and Micchelli et al. and study the necessary and sufficient conditions for a kernel to be universal. We show that all these notions of universality are closely linked to the injective embedding of a certain class of Borel measures into a reproducing kernel Hilbert space (RKHS). By exploiting this relation between universality and the embedding of Borel measures into an RKHS, we establish the relation between universal and characteristic kernels. The latter have been proposed in the context of the RKHS embedding of probability measures, used in statistical applications like homogeneity testing, independence testing, etc.
关 键 词: 普遍性的关系; 特征的内核; 算法嵌入
课程来源: 视频讲座网
最后编审: 2019-10-30:cwx
阅读次数: 86