[PDF.25ub] Algebraic Geometry and Statistical Learning Theory (Cambridge Monographs on Applied and Computational Mathematics)

Algebraic Geometry and Statistical Learning Theory (Cambridge Monographs on Applied and Computational Mathematics)

[PDF.am27] Algebraic Geometry and Statistical Learning Theory (Cambridge Monographs on Applied and Computational Mathematics)

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 | #206121 in Books |  2009-09-28 | Original language:English | PDF # 1 |  8.98 x.79 x5.98l,1.20 | File type: PDF | 300 pages

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||35 of 35 people found the following review helpful.| Excellent|By Dr. Lee D. Carlson|Statistical learning theory is now a well-established subject, and has found practical use in artificial intelligence as well as a framework for studying computational learning theory. There are many fine books on the subject, but this one studies it from the standpoint of algebraic geometry, a field which decades ago was deemed too esoteric fo| |"Overall, the many insightful remarks and simple direct language make the book a pleasure to read."
Shaowei Lin, Mathematical s|About the Author|Sumio Watanabe is a Professor in the Precision and Intelligence Labo

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.

You easily download any file type for your device.Algebraic Geometry and Statistical Learning Theory (Cambridge Monographs on Applied and Computational Mathematics)   |  Sumio Watanabe. A good, fresh read, highly recommended.

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