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Boosting foundations and algorithms

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, conv...

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Bibliographic Details
Main Authors: Schapire, Robert E. (Author), Freund, Yoav (Author)
Format: Book
Language:English
Published: Cambridge, MA. MIT Press 2014
Series:Adaptive computation and machine learning
Subjects:
Boosting (Algorithms)
Supervised learning (Machine learning)
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Table of Contents:
  • Chapter :1. Introduction and Overview
  • Chapter :I. CORE ANALYSIS
  • Chapter :2. Foundations of Machine Learning
  • Chapter :3. Using AdaBoost to Minimize Training Error
  • Chapter :4. Direct Bounds on the Generalization Error
  • Chapter :5. Margins Explanation for Boosting's Effectiveness
  • Chapter :II. FUNDAMENTAL PERSPECTIVES
  • Chapter :6. Game Theory, Online Learning, and Boosting
  • Chapter :7. Loss Minimization and Generalizations of Boosting
  • Chapter :8. Boosting, Convex Optimization, and Information Geometry
  • Chapter :III. ALGORITHMIC EXTENSIONS
  • Chapter :9. Using Confidence-Rated Weak Predictions
  • Chapter :10. Multiclass Classification Problems
  • Chapter :11. Learning to Rank
  • Chapter :IV. ADVANCED THEORY
  • Chapter :12. Attaining the Best Possible Accuracy
  • Chapter :13. Optimally Efficient Boosting
  • Chapter :14. Boosting in Continuous Time
  • Appendix: Some Notation, Definitions, and Mathematical Background.

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