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Algebraic Geometry and Statistical Learning Theory
Algebraic Geometry and Statistical Learning Theory
Date: 22 April 2011, 09:05

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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.

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