Algebraic Geometry and Statistical Learning Theory

by Sumio Watanabe

Overview

Sure to be influential, Watanabe’s book lays the foundations for the use of algebraic geometry in statistical learning theory. Many models/machines are singular: mixture models, neural networks, HMMs, Bayesian networks, stochastic context-free grammars are major examples. The theory achieved here underpins accurate estimation techniques in the presence of singularities.

Product Details

ISBN-13:	9780521864671
Publisher:	Cambridge University Press
Publication date:	08/13/2009
Series:	Cambridge Monographs on Applied and Computational Mathematics , #25
Edition description:	New Edition
Pages:	300
Product dimensions:	6.20(w) x 9.00(h) x 0.80(d)

About the Author

Sumio Watanabe is a Professor in the Precision and Intelligence Laboratory at the Tokyo Institute of Technology.

Table of Contents

Preface; 1. Introduction; 2. Singularity theory; 3. Algebraic geometry; 4. Zeta functions and singular integral; 5. Empirical processes; 6. Singular learning theory; 7. Singular learning machines; 8. Singular information science; Bibliography; Index.

Editorial Reviews

"Overall, the many insightful remarks and simple direct language make the book a pleasure to read."
Shaowei Lin, Mathematical Reviews

From the Publisher