Machine Learning in Non-Stationary Environments: Introduction to Covariate Shift Adaptation

by Motoaki Kawanabe and Masashi Sugiyama

Theory, algorithms, and applications of machine learning techniques to overcome "covariate shift" non-stationarity.

As the power of computing has grown over the past few decades, the field of machine learning has advanced rapidly in both theory and practice. Machine learning methods are usually based on the assumption that the data generation mechanism does not change over time. Yet real-world applications of machine learning, including...

• Format: Hardcover
• Language: English
• ISBN: 0262017091
• ISBN13: 9780262017091
• Release Date: January 2012
• Publisher: MIT Press
• Length: 261 Pages
• Weight: 1.10 lbs.
• Dimensions: 0.7" x 6.4" x 9.3"
• Age Range: 18 years and up
• Grade Range: Postsecondary and higher