On the Dual Formulation of Boosting Algorithms

We study boosting algorithms from a new perspective. We show that the Lagrange dual problems of ℓ 1 -norm-regularized AdaBoost, LogitBoost, and soft-margin LPBoost with generalized hinge loss are all entropy maximization problems. By looking at the dual problems of these boosting algorithms, we show...

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Veröffentlicht in:	IEEE transactions on pattern analysis and machine intelligence Jg. 32; H. 12; S. 2216 - 2231
Hauptverfasser:	Shen, Chunhua, Li, Hanxi
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Los Alamitos, CA IEEE 01.12.2010 IEEE Computer Society The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	AdaBoost Algorithms Applied sciences Artificial intelligence Australia Boosting Classification Computer science; control theory; systems Construction Convergence Data processing. List processing. Character string processing Entropy entropy maximization Exact sciences and technology Fasteners Formulations Lagrange duality Lagrangian functions Linear programming LogitBoost LPBoost Machine learning algorithms Maximization Memory organisation. Data processing Optimization Software Support vector machine classification Support vector machines Data analysis Linear programming LPBoost Optimization Variance Aggregate model Lagrange duality AdaBoost Supervised learning Classification Convergence rate LogitBoost entropy maximization Numerical convergence
ISSN:	0162-8828, 1939-3539, 1939-3539
Online-Zugang:	Volltext
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