C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
Game theory, on-line prediction and boosting
COLT '96 Proceedings of the ninth annual conference on Computational learning theory
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Boosting the margin: A new explanation for the effectiveness of voting methods
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Data Mining using MLC++, A Machine Learning Library in C++
ICTAI '96 Proceedings of the 8th International Conference on Tools with Artificial Intelligence
An empirical evaluation of bagging and boosting
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Improved Generalization Through Explicit Optimization of Margins
Machine Learning
Machine Learning
Support vector machine pairwise classifiers with error reduction for image classification
MULTIMEDIA '01 Proceedings of the 2001 ACM workshops on Multimedia: multimedia information retrieval
SVM binary classifier ensembles for image classification
Proceedings of the tenth international conference on Information and knowledge management
Machine Learning
The Role of Occam‘s Razor in Knowledge Discovery
Data Mining and Knowledge Discovery
Linear Programming Boosting via Column Generation
Machine Learning
Sparse Regression Ensembles in Infinite and Finite Hypothesis Spaces
Machine Learning
Constructing Boosting Algorithms from SVMs: An Application to One-Class Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scaling Boosting by Margin-Based Inclusionof Features and Relations
ECML '02 Proceedings of the 13th European Conference on Machine Learning
Robust Ensemble Learning for Data Mining
PADKK '00 Proceedings of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Current Issues and New Applications
EuroCOLT '99 Proceedings of the 4th European Conference on Computational Learning Theory
Theoretical Views of Boosting and Applications
ALT '99 Proceedings of the 10th International Conference on Algorithmic Learning Theory
Highlighting Hard Patterns via AdaBoost Weights Evolution
MCS '02 Proceedings of the Third International Workshop on Multiple Classifier Systems
Maximizing the Margin with Boosting
COLT '02 Proceedings of the 15th Annual Conference on Computational Learning Theory
An Architecture of a Web-Based Collaborative Image Search Engine
On the Move to Meaningful Internet Systems, 2002 - DOA/CoopIS/ODBASE 2002 Confederated International Conferences DOA, CoopIS and ODBASE 2002
Exploiting unlabeled data in ensemble methods
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Minimum majority classification and boosting
Eighteenth national conference on Artificial intelligence
An introduction to boosting and leveraging
Advanced lectures on machine learning
Online Ensemble Learning: An Empirical Study
Machine Learning
Robust feature induction for support vector machines
ICML '04 Proceedings of the twenty-first international conference on Machine learning
The Dynamics of AdaBoost: Cyclic Behavior and Convergence of Margins
The Journal of Machine Learning Research
A smoothed boosting algorithm using probabilistic output codes
ICML '05 Proceedings of the 22nd international conference on Machine learning
Unifying the error-correcting and output-code AdaBoost within the margin framework
ICML '05 Proceedings of the 22nd international conference on Machine learning
Boosting with Noisy Data: Some Views from Statistical Theory
Neural Computation
Different Paradigms for Choosing Sequential Reweighting Algorithms
Neural Computation
Neural Computation
How boosting the margin can also boost classifier complexity
ICML '06 Proceedings of the 23rd international conference on Machine learning
Totally corrective boosting algorithms that maximize the margin
ICML '06 Proceedings of the 23rd international conference on Machine learning
Efficient Margin Maximizing with Boosting
The Journal of Machine Learning Research
Unifying multi-class AdaBoost algorithms with binary base learners under the margin framework
Pattern Recognition Letters
Multi-Class Learning by Smoothed Boosting
Machine Learning
Pairwise fusion matrix for combining classifiers
Pattern Recognition
Ensemble Pruning Via Semi-definite Programming
The Journal of Machine Learning Research
Nonlinear Boosting Projections for Ensemble Construction
The Journal of Machine Learning Research
Increasing the Robustness of Boosting Algorithms within the Linear-programming Framework
Journal of VLSI Signal Processing Systems
Optimally regularised kernel Fisher discriminant classification
Neural Networks
Robust face detection in airports
EURASIP Journal on Applied Signal Processing
Boosting strategy for classification
Intelligent Data Analysis
From dynamic classifier selection to dynamic ensemble selection
Pattern Recognition
From dynamic classifier selection to dynamic ensemble selection
Pattern Recognition
The value of agreement a new boosting algorithm
Journal of Computer and System Sciences
Evidence Contrary to the Statistical View of Boosting
The Journal of Machine Learning Research
Avoiding Boosting Overfitting by Removing Confusing Samples
ECML '07 Proceedings of the 18th European conference on Machine Learning
ALT '08 Proceedings of the 19th international conference on Algorithmic Learning Theory
Exploring Margin Maximization for Biometric Score Fusion
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Supervised projection approach for boosting classifiers
Pattern Recognition
ODDboost: Incorporating Posterior Estimates into AdaBoost
MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
Towards a Linear Combination of Dichotomizers by Margin Maximization
ICIAP '09 Proceedings of the 15th International Conference on Image Analysis and Processing
A brief introduction to boosting
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Linear Programming Boosting by Column and Row Generation
DS '09 Proceedings of the 12th International Conference on Discovery Science
Boosting through optimization of margin distributions
IEEE Transactions on Neural Networks
Sparse ensembles using weighted combination methods based on linear programming
Pattern Recognition
Edited AdaBoost by weighted kNN
Neurocomputing
An algorithm on multi-view adaboost
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: theory and algorithms - Volume Part I
S-adaboost and pattern detection in complex environment
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Interpretable visual models for human perception-based object retrieval
Proceedings of the 1st ACM International Conference on Multimedia Retrieval
Selecting optimal training data for learning to rank
Information Processing and Management: an International Journal
A Refined Margin Analysis for Boosting Algorithms via Equilibrium Margin
The Journal of Machine Learning Research
Margin distribution based bagging pruning
Neurocomputing
Classifier ensemble using a heuristic learning with sparsity and diversity
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part II
Expert Systems with Applications: An International Journal
On the doubt about margin explanation of boosting
Artificial Intelligence
GA-Ensemble: a genetic algorithm for robust ensembles
Computational Statistics
The rate of convergence of AdaBoost
The Journal of Machine Learning Research
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The "minimum margin" of an ensemble classifier on a given training set is, roughly speaking, the smallest vote it gives to any correct training label. Recent work has shown that the Adaboost algorithm is particularly effective at producing ensembles with large minimum margins, and theory suggests that this may account for its success at reducing generalization error. We note, however, that the problem of finding good margins is closely related to linear programming, and we use this connection to derive and test new "LPboosting" algorithms that achieve better minimum margins than Adaboost.However, these algorithms do not always yield better generalization performance. In fact, more often the opposite is true. We report on a series of controlled experiments which show that no simple version of the minimum-margin story can be complete. We conclude that the crucial question as to why boosting works so well in practice, and how to further improve upon it, remains mostly open.Some of our experiments are interesting for another reason: we show that Adaboost sometimes does overfit--eventually. This may take a very long time to occur, however, which is perhaps why this phenomenon has gone largely unnoticed.