Boosting is a general method for improving the accuracy of any given learning algorithm.
Focusing primarily on the AdaBoost algorithm, this chapter overviews some of the recent …

Divergence measures for statistical data processing—An annotated bibliography -
ScienceDirect Skip to main contentSkip to article Elsevier logo Journals & Books Search …

The term “boosting” denotes a powerful means of facilitating machine learning that was
invented by the book's authors 20 years ago and intensively developed since. Despite this …

A new edition of a graduate-level machine learning textbook that focuses on the analysis
and theory of algorithms. This book is a general introduction to machine learning that can …

This is the first comprehensive book on information geometry, written by the founder of the
field. It begins with an elementary introduction to dualistic geometry and proceeds to a wide …

Many of the classification algorithms developed in the machine learning literature, including
the support vector machine and boosting, can be viewed as minimum contrast methods that …

An important way to make large training sets is to gather noisy labels from crowds of
nonexperts. We propose a minimax entropy principle to improve the quality of these labels …

Most optimization methods for logistic regression or maximum entropy solve the primal
problem. They range from iterative scaling, coordinate descent, quasi-Newton, and …

A new" herding" algorithm is proposed which directly converts observed moments into a
sequence of pseudo-samples. The pseudo-samples respect the moment constraints and …

This article considers approaches which rerank the output of an existing probabilistic parser.
The base parser produces a set of candidate parses for each input sentence, with …