Algebraic Geometry and Statistical Learning Theory

by Sumio Watanabe

Book Overview

Sure to be influential, this book lays the foundations for the use of algebraic geometry in statistical learning theory. Many widely used statistical models and learning machines applied to information science have a parameter space that is singular: mixture models, neural networks, HMMs, Bayesian networks, and stochastic context-free grammars are major examples. Algebraic geometry and singularity theory provide the necessary tools for studying such...

• Format: Hardcover
• Language: English
• ISBN: 0521864674
• ISBN13: 9780521864671
• Release Date: September 2009
• Publisher: Cambridge University Press
• Length: 300 Pages
• Weight: 2.40 lbs.
• Dimensions: 0.8" x 6.0" x 9.0"