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Parameters
- 300 pages
- 11 hours of reading
More about the book
Sure to be influential, Watanabe’s book lays the foundations for the use of algebraic geometry in statistical learning theory. Many models/machines are 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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Algebraic Geometry and Statistical Learning Theory, Sumio Watanabe
- Language
- Released
- 2008
- product-detail.submit-box.info.binding
- (Hardcover)
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- Title
- Algebraic Geometry and Statistical Learning Theory
- Language
- English
- Authors
- Sumio Watanabe
- Publisher
- Cambridge University Press
- Released
- 2008
- Format
- Hardcover
- Pages
- 300
- ISBN10
- 0521864674
- ISBN13
- 9780521864671
- Tags
- Non-Fiction, Technology & Engineering, Science & Math, Computers & Internet, Science, Mathematics, Math Textbooks, Professional Literature, Artificial Intelligence, Geometry, Machine Learning
- Rating
- 4.45 out of 5
- Description
- Sure to be influential, Watanabe’s book lays the foundations for the use of algebraic geometry in statistical learning theory. Many models/machines are 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.