Algebraic Geometry and Statistical Learning Theory

Authors:
Sumio Watanabe
Affiliations:
-
Venue:
Algebraic Geometry and Statistical Learning Theory
Year:
2009

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Abstract

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.