Algebraic Geometry and Statistical Learning Theory
Algebraic Geometry and Statistical Learning Theory
Looking for an examination copy?
This title is not currently available for examination. However, if you are interested in the title for your course we can consider offering an examination copy. To register your interest please contact [email protected] providing details of the course you are teaching.
$106.00
USDSure 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.
- Presents a new statistical theory for singular learning machines ● Mathematical concepts explained for non-specialists ● Intended for any student interested in machine learning, pattern recognition, artificial intelligence or bioinformatics
Reviews & endorsements
"Overall, the many insightful remarks and simple direct language make the book a pleasure to read."
Shaowei Lin, Mathematical Reviews
Product details
November 2009Adobe eBook Reader
9780511601613
0 pages
0kg
13 b/w illus.
This ISBN is for an eBook version which is distributed on our behalf by a third party.
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.
