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 gramm
CMSC 754 Computational Geometry1 David M. Mount Department of Computer Science University of Maryland Spring 2007 Copyright, David M. Mount, 2007, Dept. of Computer Science, University of Maryland, College Park, MD, 20742. These lecture notes were p
The subject of Discrete Geometry and Convex Polytopes has received much attention in recent decades, with the explosion of the work in the field. This book is aimed to be an introduction to some of our favorite parts of the subject, covering some fa
carroll, space and geometry. djvu editioncarroll, space and geometry. djvu editioncarroll, space and geometry. djvu editioncarroll, space and geometry. djvu edition
Statistical machine learning algorithms deal with the problem of selecting an appropriate statistical model from a model space based on a training set {xi}N i=1 ⊂ X or {(xi, yi)}N i=1 ⊂ X × Y. In doing so they either implicitly or explicitly make
