[PDF.83mi] Algebraic Geometry and Statistical Learning Theory (Cambridge Monographs on Applied and Computational Mathematics)
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Algebraic Geometry and Statistical Learning Theory (Cambridge Monographs on Applied and Computational Mathematics)
Sumio Watanabe
[PDF.vv27] Algebraic Geometry and Statistical Learning Theory (Cambridge Monographs on Applied and Computational Mathematics)
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| #861395 in Books | 2009-09-28 | Original language:English | PDF # 1 | 8.98 x.79 x5.98l,1.20 | File type: PDF | 300 pages||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.
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