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Boosting : foundations and algorithms /
A remarkably rich theory has evolved around boosting, with connections to a range of topics including statistics, game theory, convex optimization, and information geometry. Boosting algorithms have also enjoyed practical success in such fields as biology, vision, and speech processing. At various t...
| Call Number: | Libro Electrónico |
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| Main Author: | |
| Other Authors: | |
| Format: | Electronic eBook |
| Language: | Inglés |
| Published: |
Cambridge, MA :
MIT Press,
©2012.
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| Series: | Adaptive computation and machine learning.
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| Subjects: | |
| Online Access: | Texto completo |
Table of Contents:
- Foundations of machine learning
- Using AdaBoost to minimize training error
- Direct bounds on the generalization error
- The margins explanation for boosting's effectiveness
- Game theory, online learning, and boosting
- Loss minimization and generalizations of boosting
- Boosting, convex optimization, and information geometry
- Using confidence-rated weak predictions
- Multiclass classification problems
- Learning to rank
- Attaining the best possible accuracy
- Optimally efficient boosting
- Boosting in continuous time.