Minimum Divergence Methods in Statistical Machine Learning From an Inf

Minimum Divergence Methods in Statistical Machine Learning From an Inf Description

This book explores minimum divergence methods of statistical machine learning for estimation, nbspregression, prediction, and so forth, nbspin which we engage in information geometry to elucidate their intrinsic properties of the corresponding loss functions, learning algorithms, and statistical models.nbspOne of the most elementary nbspexamples is Gauss's least squares estimator in a linear regression model, in which the estimator is given by minimization of the sum of squares between a