Kernel Method, Kernel Mean Embedding 相关资源整理

1. 几个重要的函数空间，Hilbert Spaces，L_p Spaces, Holder Spaces, Mercer Kernels 和 Reproducing Kernel Hilbert Spaces。参考文档：Function Spaces。该文档对理解RHKS比较抽象。

2. 概述论文Muandet K, Fukumizu K, Sriperumbudur B, et al. Kernel mean embedding of distributions: A review and beyond[J]. Foundations and Trends® in Machine Learning, 2017, 10(1-2): 1-141. 论文链接

3. 一个课程
   Reproducing kernel Hilbert spaces in Machine Learning，地址:http://www.gatsby.ucl.ac.uk/~gretton/coursefiles/rkhscourse.html
   该课程包括的notes:

• 引入RHKS Introduction to RKHS, and some simple kernel algorithms
• Notes on mean embeddings and covariance operators
• the maximum mean discrepancy (MMD)
• Hilbert-Schmidt independence criterion HSIC
• 核函数在支持向量机中的应用SVM

其它文档后续补充...