Boosting the margin: A new explanation for the effectiveness of voting methods

Keywords

Boosting (machine learning), Mathematics, Voting, Margin (machine learning), Pattern recognition (psychology)

References

• Programs for Machine LearningSteven L. Salzberg, Alberto M. Segre — 1994
• Bias Plus Variance Decomposition for Zero-One Loss FunctionsRon Kohavi, David H. Wolpert — 1996
• Improving Regressors using Boosting TechniquesHarris Drucker — 1997
• Using output codes to boost multiclass learning problemsRobert E. Schapire — 1997
• Classification and regression treesAlexander Gordon, Leo Breiman, Jerome H. Friedman — 1983
• Solving Multiclass Learning Problems via Error-Correcting Output CodesTom Dietterich, Ghulum Bakiri — 1994
• An Empirical Evaluation of Bagging and BoostingRichard Maclin, David W. Opitz — 1997
• On the Density of Families of SetsN. Sauer — 1972
• The importance of convexity in learning with squared lossWee Sun Lee, Peter L. Bartlett, Robert C. Williamson — 1998
• A decision-theoretic generalization of on-line learning and an application to boostingYoav Freund, Robert E. Schapire — 1997
• Improved Boosting Algorithms Using Confidence-rated PredictionsRobert E. Schapire, Yoram Singer — 1998
• A Simple Lemma on Greedy Approximation in Hilbert Space and Convergence Rates for Projection Pursuit Regression and Neural Network TrainingLee Jones — 1992
• Boosting a weak learning algorithm by majorityYoav Freund — 1990
• Rates of convex approximation in non-hilbert spacesM. J. Donahue, Chris Darken, Leonid Gurvits — 1997
• A training algorithm for optimal margin classifiersBernhard E. Boser, Isabelle Guyon, Vladimir Vapnik — 1992
• The Strength of Weak LearnabilityRobert E. Schapire — 1989
• A framework for structural risk minimisationJohn Shawe‐Taylor, Peter L. Bartlett, Robert C. Williamson — 1996
• Game theory, on-line prediction and boostingYoav Freund, Robert E. Schapire — 1996
• The Sample Complexity of Pattern Classification with Neural Networks: The Size of the Weights is More Important than the Size of the NetworkPeter L. Bartlett — 1998
• Boosting in the Limit: Maximizing the Margin of Learned EnsemblesAdam J. Grove, Dale Schuurmans — 1998

Cited by

• Foundations of Machine LearningMehryar Mohri, Afshin Rostamizadeh, Ameet Talwalkar — 2012
• Support Vector Machine Concept-Dependent Active Learning for Image RetrievalEdward Yi Chang, Simon Tong, Chengwei Chang — 2005
• On Combining Classifiers for Assessing Portrait Image Compliance with ICAO/ISO StandardsMarkus Storer, Martin Urschler, Horst Bischof — 2008
• Pruning and Exclusion Criteria for Unordered Incremental Reduced Error PruningHenrik Boström — 2004
• A Simple Algorithm for Learning Stable MachinesS. Andonova, André Elisseeff, Theodoros Evgeniou — 2002
• From patterns to pathways: gene expression data analysis comes of ageDonna K. Slonim — 2002
• Building a cascade detector and its applications in automatic target detection.Susan Dean — 2004
• Dynamic classifier ensemble using classification confidenceLeijun Li, Bo Zou, Qinghua Hu — 2013
• Labeling hypergraph-structured data using markov networkA. Elgammal, T. Parag — 2011
• Feature construction from synergic pairs to improve microarray-based classificationBlaise Hanczar, Jean‐Daniel Zucker, Corneliu Hénégar — 2007
• Parallelizing support vector machines for scalable image annotationNasullah Khalid Alham — 2011
• Secession and Survival: Nations, States and Violent ConflictDavid S. Siroky — 2009
• A new approach to assess and predict the functional roles of proteins across all known structuresSantiago Javier Armesilla Conde — 2011
• Moving towards predictive toxicology - a systems biology approachPhilipp Antczak — 2012
• An ensemble method using hybrid real-coded genetic algorithm with pruning (HRGA/P R )Hong Zhang, Masumi Ishikawa — 2007
• Chagas Parasite Detection in Blood Images Using AdaBoostSabina Berman — 2015
• Ensemble Methods for Structured PredictionCorinna Cortes, Vitaly Kuznetsov, Mehryar Mohri — 2014
• Machine Learning to Predict the Incidence of Retinopathy of PrematurityAniket Ray, Vikas Kumar, Balaraman Ravindran — 2008
• Automatic Metadata Annotation of Images via a Two-Level Learning FrameworkChih‐Fong Tsai, Kenneth McGarry, John Tait — 2004
• Breakdown Point of Robust Support Vector MachinesTakafumi Kanamori, Shuhei Fujiwara, Akiko Takeda — 2014

Related papers

• Researching on combining boosting ensemblesJoaquín Torres-Sospedra, Carlos Hernández-Espinosa, Mercedes Fernández-Redondo — 2008
• A Brief Introduction to BoostingRobert E. Schapire — 1999
• Resampling or Reweighting: A Comparison of Boosting ImplementationsChris Seiffert, Taghi M. Khoshgoftaar, Jason Van Hulse — 2008
• When is voting optimal?Ruth Ben‐Yashar, Leif Danziger — 2014
• On the Optimality of DecisionsJan Marc Berk, Beata K. Bierut — 2004
• Measuring majority power and veto power of voting rulesAleksei Y. Kondratev, Alexander Nesterov — 2018
• Decision making with Clustered Majority JudgmentArianna Anniciello, Emanuele d’Ajello, Davide Formica — 2022
• Novel Bounds on the Probability of Misclassification in Majority Voting: Leveraging the Majority SizeA.T.J.R. Cobbenhagen, Algo Carè, Marco C. Campi — 2021