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Algebraic geometry and statistical learning theory by Sumio Watanabe

Algebraic geometry and statistical learning theory Sumio Watanabe ebook
Publisher: CUP
ISBN: 0521864674, 9780521864671
Page: 296
Format: pdf

With the rise of high-dimensional machine learning, these fields are increasing being pulled into interesting computational applications such as manifold learning. Connections of this type are at the heart of the new field of "algebraic statistics". Algorithms in algebraic geometry go hand in hand with software packages that implement them. Download · Email ThisBlogThis!Share to TwitterShare to Facebook. More >>; Zakon Elias, Mathematical Analysis II, Trillia Group 2009. Algebraic Geometry and Statistical Learning Theory, Cambridge University Press 2009. Algebraic Geometry and Statistical Learning Theory – Computers. Information Geometry: Methods of Information Geometry Shun-Ichi Amari, Hiroshi Nagaoka Algebraic Geometry and Statistical Learning Theory Watanabe, Sumio Differential Geometry and Statistics M.K. Watanabe's book lays the foundations for the use of algebraic geometry in statistical learning theory. This book examines all aspects essential for a successful application of geometric algebra: the theoretical foundations, the representation of geometric constraints, and the numerical estimation from uncertain data. Intersection of Algebraic Topology/Geometry and Model Theory/Set. Algebraic.Geometry.and.Statistical.Learning.Theory.296p.2009.Cambridge.pdf.