A geometric approach to leveraging weak learners

DOI10.1016/S0304-3975(01)00083-4zbMath0997.68166MaRDI QIDQ1603593

Nigel Duffy, David P. Helmbold

Publication date: 15 July 2002

Published in: Theoretical Computer Science (Search for Journal in Brave)

zbMATH Keywords

classificationlearningboostingensemble methodsgradient descent

Mathematics Subject Classification ID

Nonnumerical algorithms (68W05) Learning and adaptive systems in artificial intelligence (68T05)

Related Items

Bounding the generalization error of convex combinations of classifiers: Balancing the dimensionality and the margins., Random classification noise defeats all convex potential boosters, The synergy between PAV and AdaBoost, Analysis of boosting algorithms using the smooth margin function, Greedy function approximation: A gradient boosting machine., The synergy between PAV and AdaBoost, Improving nonparametric regression methods by bagging and boosting.

Uses Software

• UCI-ml
• MLC++

Cites Work

• Unnamed Item
• Bagging predictors
• Estimation of dependences based on empirical data. Transl. from the Russian by Samuel Kotz
• Equivalence of models for polynomial learnability
• A decision-theoretic generalization of on-line learning and an application to boosting
• Boosting the margin: a new explanation for the effectiveness of voting methods
• Additive logistic regression: a statistical view of boosting. (With discussion and a rejoinder by the authors)
• Boosting a weak learning algorithm by majority
• Improved boosting algorithms using confidence-rated predictions
• An approach to nonlinear programming
• A theory of the learnable
• Learning Boolean formulas
• Prediction Games and Arcing Algorithms
• Soft margins for AdaBoost
• General convergence results for linear discriminant updates
• Stochastic gradient boosting.