Universal Features for High-Dimensional Learning and Inference

by Gregory W Wornell, Shao-Lun Huang, Anuran Makur

In many contemporary and emerging applications of machine learning and statistical inference, the phenomena of interest are characterized by variables defined over large alphabets. This increasing size of both the data and the number of inferences, and the limited available training data means there is a need to understand which inference tasks can be most effectively carried out, and, in turn, what features of the data are most relevant to them...

• Format: Paperback
• Language: English
• ISBN: 1638281769
• ISBN13: 9781638281764
• Release Date: February 2024
• Publisher: Now Publishers
• Length: 320 Pages
• Weight: 0.99 lbs.
• Dimensions: 0.7" x 6.1" x 9.2"