Algebraic Geometry and Statistical Learning Theory
(Part of the Cambridge Monographs on Applied and Computational Mathematics Series)
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Book Overview
Sure to be influential, this book lays the foundations for the use of algebraic geometry in statistical learning theory. Many widely used statistical models and learning machines applied to information science have a parameter space that is singular: mixture models, neural networks, HMMs, Bayesian networks, and stochastic context-free grammars are major examples. Algebraic geometry and singularity theory provide the necessary tools for studying such...
Format:Hardcover
Language:English
ISBN:0521864674
ISBN13:9780521864671
Release Date:September 2009
Publisher:Cambridge University Press
Length:300 Pages
Weight:2.40 lbs.
Dimensions:0.8" x 6.0" x 9.0"
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