Kernel Mean Embedding of Distributions

A Review and Beyond
1. Auflage 2017
ISBN: 978-1-68083-288-4
Verlag: Now Publishers

Buch, Englisch, Band 31, 154 Seiten, Format (B × H): 156 mm x 234 mm

Reihe: Foundations and Trends® in Machine Learning

This monograph provides a comprehensive review of kernel mean embeddings of distributions and, in the course of doing so, discusses some challenging issues that could potentially lead to new research directions. 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.

Autoren/Hrsg.

Muandet, Krikamol

Fukumizu, Kenji

Sriperumbudur, Bharath

Schölkopf, Bernhard

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen

Inhaltsverzeichnis

1: Introduction 2: Background 3: Hilbert Space Embedding of Marginal Distributions 4: Hilbert Space Embedding of Conditional Distributions 5: Relationships between KME and Other Methods 6: Future Directions 7: Conclusions. References.