Unifying the error-correcting and output-code AdaBoost within the margin framework

Keywords

AdaBoost, Margin (machine learning), Artificial intelligence, Computer science, Pattern recognition (psychology)

References

• Special Invited Paper-Additive logistic regression: A statistical view of boostingJerome H. Friedman — 2000
• Using output codes to boost multiclass learning problemsRobert E. Schapire — 1997
• On the Learnability and Design of Output Codes for Multiclass ProblemsKoby Crammer, Yoram Singer — 2002
• An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and RandomizationThomas G. Dietterich — 2000
• Solving Multiclass Learning Problems via Error-Correcting Output CodesTom Dietterich, Ghulum Bakiri — 1994
• Greedy function approximation: A gradient boosting machine.Jerome H. Friedman — 2001
• The Dynamics of AdaBoost: Cyclic Behavior and Convergence of MarginsCynthia Rudin, Ingrid Daubechies, Robert E. Schapire — 2004
• Multiclass learning, boosting, and error-correcting codesVenkatesan Guruswami, Amit Sahai — 1999
• Boosting the margin: A new explanation for the effectiveness of voting methodsPeter L. Bartlett, Yoav Freund, Wee Sun Lee — 1997
• UCI Repository of machine learning databasesCatherine Blake — 1998
• Boosting in the Limit: Maximizing the Margin of Learned EnsemblesAdam J. Grove, Dale Schuurmans — 1998
• Reducing multiclass to binary: a unifying approach for margin classifiersErin L. Allwein, Robert E. Schapire, Y. Singer — 2001
• C4.5: Programs for Machine LearningJ. R. Quinlan — 1992
• Prediction Games and Arcing AlgorithmsLeo Breiman — 1999
• An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, BThomas G. Dietterich — 1998
• Functional Gradient Techniques for Combining HypothesesLlew Mason, Jonathan Baxter, P. Bartlett — 2000
• Santa Cruz, CaliforniaGeo. R. Lawrence Co.

Cited by

• Iterative RELIEF for Feature Weighting: Algorithms, Theories, and ApplicationsÁngel Jesús Lázaro Martínez, Jesús Asensi Díaz — 2007
• Boosting Algorithms: A Review of Methods, Theory, and ApplicationsArtur Ferreira, Mário A. T. Figueiredo — 2012
• Outil d'aide au diagnostic du cancer à partir d'extraction d'informations issues de bases de données et d'analyses par biopucesLyamine Hedjazi — 2011
• Feature phenomenology and feature extraction of civilian vehicles from SAR imagesChristopher Paulson, Dapeng Wu — 2011
• Fusion of systems for automated cell phenotype image classificationLoris Nanni, Alessandra Lumini, Yu-Shi Lin — 2010
• Error-resilient pattern classification using a combination of spreading and coding gainsAmr El-Helw, M. Moniri, Claude C. Chibelushi — 2007
• Similarity-margin based feature selection for symbolic interval dataLyamine Hedjazi, Joseph Aguilar-Martín, Marie‐Véronique Le Lann — 2011
• Transforming examples for multiclass boostingTom Bylander — 2010
• Multi-class pattern learning using spread spectrum codesAmr El-Helw, M. Moniri, Claude C. Chibelushi — 2006
• Local Supervised Learning through Space PartitioningJoseph Wang, Venkatesh Saligrama — 2012
• Multiclass boosting with repartitioningLing Li — 2006
• Boosting multiclass learning with repeating codes and weak detectors for protein subcellular localizationChung‐Chih Lin, Yuh‐Show Tsai, Yu-Shi Lin — 2007
• Efficient scale space auto-context for image segmentation and labelingJiayan Jiang, Zhuowen Tu — 2009
• Data Complexity in Machine Learning and Novel Classification AlgorithmsYaser S. Abu‐Mostafa, Ling Li — 2006
• Generalized Multiclass AdaBoost and Its Applications to Multimedia ClassificationWei Hao, Jiebo Luo — 2006
• Multiclass Boosting with Hinge Loss based on Output CodingTianshi Gao, Daphne Koller — 2011
• Finding shareable informative patterns and optimal coding matrix for multiclass boostingBang Zhang, Getian Ye, Yang Wang — 2009
• A Simple Multi-Class Boosting Framework with Theoretical Guarantees and Empirical ProficiencyRon D. Appel, Pietro Perona — 2017
• Multiclass Boosting: Margins, Codewords, Losses, and AlgorithmsMohammad Saberian, Nuno Vasconcelos — 2019
• The Study of Multiple Classes Boosting Classification Method Based on Local SimilarityShixun Wang, Qiang Chen — 2021

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