Boosting: Foundations and Algorithms
by Robert E. Schapire, Yoav Freund
Publisher: The MIT Press 2014
ISBN-13: 9780262310413
Number of pages: 544
Description:
Boosting is an approach to machine learning based on the idea of creating a highly accurate predictor by combining many weak and inaccurate 'rules of thumb'. A remarkably rich theory has evolved around boosting, with connections to a range of topics, including statistics, game theory, convex optimization, and information geometry.
Download or read it online for free here:
Download link
(16MB, PDF)
Similar books
Optimal and Learning Control for Autonomous Robotsby Jonas Buchli, et al. - arXiv.org
The starting point is the formulation of of an optimal control problem and deriving the different types of solutions and algorithms from there. These lecture notes aim at supporting this unified view with a unified notation wherever possible.
(7826 views)
Statistical Foundations of Machine Learningby Gianluca Bontempi, Souhaib Ben Taieb
This handbook aims to present the statistical foundations of machine learning intended as the discipline which deals with the automatic design of models from data. This manuscript aims to find a good balance between theory and practice.
(11405 views)
Practical Artificial Intelligence Programming in Javaby Mark Watson - Lulu.com
The book uses the author's libraries and the best of open source software to introduce AI (Artificial Intelligence) technologies like neural networks, genetic algorithms, expert systems, machine learning, and NLP (natural language processing).
(28186 views)
The LION Way: Machine Learning plus Intelligent Optimizationby Roberto Battiti, Mauro Brunato - Lionsolver, Inc.
Learning and Intelligent Optimization (LION) is the combination of learning from data and optimization applied to solve complex problems. This book is about increasing the automation level and connecting data directly to decisions and actions.
(40409 views)