Algebraic Geometry and Statistical Learning Theory

Cambridge Monographs on Applied and Computational Mathematics

by Sumio Watanabe

Sure to be influential, Watanabe’s book lays the foundations for the use of algebraic geometry in statistical learning theory. Many models/machines are 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.

Genres: Mathematics, Textbooks

300 pages, Hardcover

First published August 1, 2009