Algebraic Geometry and Statistical Learning Theory

Name: Algebraic Geometry and Statistical Learning Theory
Availability: InStock
ISBN: 9780521864671

ISBN-10: 0521864674

ISBN-13: 9780521864671

Edition: 2009

Authors: Sumio Watanabe

List price: $129.95

This item qualifies for FREE shipping.

30 day, 100% satisfaction guarantee!

Sell

Get cash fast!

Semester Rental: $75.47 Due date: 8/14/2024

Rental notice: supplementary materials (access codes, CDs, etc.) are not guaranteed with rental orders.

Buy new: $70.08

Marketplace

2 new & used from $91.59

what's this?

Rush Rewards U
Members Receive:

You have reached 400 XP and carrot coins. That is the daily max!

Book details

List price: $129.95
Copyright year: 2009
Publisher: Cambridge University Press
Publication date: 8/13/2009
Binding: Hardcover
Pages: 300
Size: 6.10" wide x 9.17" long x 0.79" tall
Weight: 1.232
Language: English

Sumio Watanabe is a Professor in the Precision and Intelligence Laboratory at the Tokyo Institute of Technology.



Preface



Introduction



Basic concepts in statistical learning



Statistical models and learning machines



Statistical estimation methods



Four main formulas



Overview of this book



Probability theory



Singularity theory



Polynomials and analytic functions



Algebraic set and analytic set



Singularity



Resolution of singularities



Normal crossing singularities



Manifold



Algebraic geometry



Ring and ideal



Real algebraic set



Singularities and dimension



Real projective space



Blow-up



Examples



Zeta function and singular integral



Schwartz distribution



State density function



Mellin transform



Evaluation of singular integral



Asymptotic expansion and b-function



Empirical processes



Convergence in law



Function-valued analytic functions



Empirical process



Fluctuation of Gaussian processes



Singular learning theory



Standard form of likelihood ratio function



Evidence and stochastic complexity



Bayes and Gibbs estimation



Maximum likelihood and a posteriori



Singular learning machines



Learning coefficient



Three-layered neural networks



Mixture models



Bayesian network



Hidden Markov model



Singular learning process



Bias and variance



Non-analytic learning machines



Singular statistics



Universally optimal learning



Generalized Bayes information criterion



Widely applicable information criteria



Singular hypothesis test



Realization of a posteriori distribution



From regular to singular


Bibliography


Index