Boosting
Models, Applications and Extensions
1st Edition
Published on 15. June 2017
Book
Hardback
288 pages
978-1-4398-1415-4 (ISBN)
Description
Written by the developers of the first practical boosting algorithm, AdaBoost, this reference covers the background, theory, and advances in the formula. The first part of the book provides a general background of the subject. It is followed by an outline of the theory of boosting and the extensions to the AdaBoost algorithm that have been made since its inception. Chapters cover the mathematical study of machine learning, analysis of AdaBoost's training error, the generalization error, and game theory. The authors also discuss specific applications, such as bioinformatics and computer vision, and provide examples to explain topics and ensure understanding.
More details
Language
English
Place of publication
Bosa Roca
United States
Publishing group
Taylor & Francis Inc
Target group
Researchers and graduate students in statistics, machine learning, bioinformatics, computer vision, and engineering.
Illustrations
50 s/w Abbildungen
50 Illustrations, black and white
Dimensions
Height: 234 mm
Width: 156 mm
ISBN-13
978-1-4398-1415-4 (9781439814154)
Copyright in bibliographic data is held by Nielsen Book Services Limited or its licensors: all rights reserved.
Schweitzer Classification
Persons
Robert E. Schapire, Department of Computer Science, Princeton University, New Jersey, USA
Yoav Freund, Department of Computer Science and Engineering, University of California, San Diego, USA
Yoav Freund, Department of Computer Science and Engineering, University of California, San Diego, USA
Content
Introduction. The Mathematical Study of Machine Learning. Analyzing AdaBoost's Training Error. A First Bound on the Generalization Error. Using Margins to Analyze the Generalization Error. Boosting and Game Theory. Ranking Problems.