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Algebraic geometry and statistical learning theory by Sumio Watanabe

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

More specifically, the author uses the resolution of singularities theorem from real algebraic geometry to study statistical learning theory when the parameter space is highly singular. There's a (involved) book “Algebraic Geometry and Statistical Learning Theory” by Sumio Watanabe which beyond above also develops (not terribly practical at the moment) methods for graphical models from that viewpoint. This means we learn Algebra, Geometry, Statistics, and Number Theory in increasing complexity each year. Algebraic Geometry and Statistical Learning Theory. What assumptions are we as humans making that differ from those in learning theory? An Elementary Introduction to Statistical Learning Theory is an excellent book for courses on statistical learning theory, pattern recognition, and machine learning at the upper-undergraduat. Connections of this type are at the heart of the new field of "algebraic statistics". With the rise of high-dimensional machine learning, these fields are increasing being pulled into interesting computational applications such as manifold learning. These assumptions are probably something stronger than distribution-independent (e.g. Which means that some students enter my class having already studied Algebra.