Algebraic Geometry and Statistical Learning Theory
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.
- 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
September 2009Hardback
9780521864671
300 pages
233 × 155 × 20 mm
0.56kg
13 b/w illus.
Available
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.