The Annals of Statistics, 32(1), February, A Discussion of: “Process Consistency for AdaBoost” by Wenxin Jiang “On the Bayes-risk consistency of regularized boosting methods” by ´bor Lugosi and Nicolas Va

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CS269: Machine Learning Theory Lecture 14: Generalization Error of Adaboost November 10, 2010 Lecturer: Jennifer Wortman Vaughan Scribe: David Lei, Daniel Quach, Alex Lee In this lecture we will continue our discussion o

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Journal of Machine Learning Research2347 Submitted 8/11; Revised 5/13; Published 8/13 The Rate of Convergence of AdaBoost Indraneel Mukherjee

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CS 559: Homework Set 4 Due: December 3, 6:00pm Philippos Mordohai Department of Computer Science Stevens Institute of Technology

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• Machine learning
• Artificial intelligence
• Learning
• Ensemble learning
• Statistical classification
• Boosting
• AdaBoost
• Support vector machine
• Margin
• LIBSVM

The Convergence Rate of AdaBoost Robert E. Schapire Princeton University Department of Computer Science

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Journal of Machine Learning Research2232 Submitted 2/08; Revised 7/09; PublishedMargin-based Ranking and an Equivalence between AdaBoost and RankBoost

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Machine Learning, ), Logistic Regression, AdaBoost and Bregman Distances Michael Collins AT&T Labs Research Shannon Laboratory

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Explaining AdaBoost Robert E. Schapire Abstract Boosting is an approach to machine learning based on the idea of creating a highly accurate prediction rule by combining many relatively weak and inaccurate rules. The AdaB

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Classifying Skewed Data: Importance Weighting to Optimize Average Recall Andrew Rosenberg1 1 Department of Computer Science, Queens College (CUNY), Flushing, NY, USA

Source URL: speech.cs.qc.cuny.edu

• Statistical classification
• Machine learning
• Ensemble learning
• Data mining
• K-nearest neighbors algorithm
• Multiclass classification
• Boosting
• Pitch accent
• Support vector machine
• AdaBoost
• Receiver operating characteristic
• Binary classification

Contemporary Mathematics Precise Statements of Convergence for AdaBoost and arc-gv Cynthia Rudin, Robert E. Schapire, and Ingrid Daubechies We wish to dedicate this paper to Leo Breiman. Abstract. We present two main re

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