Scheduled Maintenance on May 24th, 2018:
IEEE Xplore will undergo system maintenance from 1:30 PM - 3:00 PM ET. During this time there may be intermittent impact on performance. We apologize for any inconvenience.
Cover Image

Kernel Mean Embedding of Distributions:A Review and Beyond

The purchase and pricing options for this item are unavailable. Select items are only available as part of a subscription package. You may try again later or contact us for more information.
4 Author(s)

A Hilbert space embedding of a distribution¿¿¿in short, a kernel mean embedding¿¿¿has recently emerged as a powerful tool for machine learning and statistical inference. The basic idea behind this framework is to map distributions into a reproducing kernel Hilbert space (RKHS) in which the whole arsenal of kernel methods can be extended to probability measures. It can be viewed as a generalization of the original ¿¿¿feature map¿¿? common to support vector machines (SVMs) and other kernel methods. In addition to the classical applications of kernel methods, the kernel mean embedding has found novel applications in ¿¿?elds ranging from probabilistic modeling to statistical inference, causal discovery, and deep learning. Kernel Mean Embedding of Distributions: A Review and Beyond provides a comprehensive review of existing work and recent advances in this research area, and to discuss some of the most challenging issues and open problems that could potentially lead to new research direct ons. The targeted audience includes graduate students and researchers in machine learning and statistics who are interested in the theory and applications of kernel mean embeddings.