The Strength of Weak Learnability
Machine Learning
Boosting a weak learning algorithm by majority
Information and Computation
Machine Learning
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
Improved Boosting Algorithms Using Confidence-rated Predictions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Machine Learning
COLT '00 Proceedings of the Thirteenth Annual Conference on Computational Learning Theory
Machine Learning
An introduction to boosting and leveraging
Advanced lectures on machine learning
Boosting grammatical inference with confidence oracles
ICML '04 Proceedings of the twenty-first international conference on Machine learning
The mean subjective utility score, a novel metric for cost-sensitive classifier evaluation
Pattern Recognition Letters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multi-Class Learning by Smoothed Boosting
Machine Learning
High-Performance Rotation Invariant Multiview Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Random classification noise defeats all convex potential boosters
Proceedings of the 25th international conference on Machine learning
Avoiding Boosting Overfitting by Removing Confusing Samples
ECML '07 Proceedings of the 18th European conference on Machine Learning
A Boost Voting Strategy for Knowledge Integration and Decision Making
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks
Patient-centered yes/no prognosis using learning machines
International Journal of Data Mining and Bioinformatics
Extracting Auto-Correlation Feature for License Plate Detection Based on AdaBoost
IDEAL '08 Proceedings of the 9th International Conference on Intelligent Data Engineering and Automated Learning
An empirical comparison of three boosting algorithms on real data sets with artificial class noise
MCS'03 Proceedings of the 4th international conference on Multiple classifier systems
Learning-based license plate detection in vehicle image database
International Journal of Intelligent Information and Database Systems
Edited AdaBoost by weighted kNN
Neurocomputing
A low variance error boosting algorithm
Applied Intelligence
Face detection with effective feature extraction
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part III
SpatialBoost: adding spatial reasoning to adaboost
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Real-Time license plate detection under various conditions
UIC'06 Proceedings of the Third international conference on Ubiquitous Intelligence and Computing
Learning outliers to refine a corpus for chinese webpage categorization
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
Defect cluster recognition system for fabricated semiconductor wafers
Engineering Applications of Artificial Intelligence
A theory of multiclass boosting
The Journal of Machine Learning Research
Smoothed emphasis for boosting ensembles
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advances in computational intelligence - Volume Part I
Robust feature selection based on regularized brownboost loss
Knowledge-Based Systems
GA-Ensemble: a genetic algorithm for robust ensembles
Computational Statistics
Algorithms and hardness results for parallel large margin learning
The Journal of Machine Learning Research
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We propose a new boosting algorithm. This boosting algorithm is an adaptive version of the boost by majority algorithm and combines bounded goals of the boost by majority algorithm with the adaptivity of AdaBoost.The method used for making boost-by-majority adaptive is to consider the limit in which each of the boosting iterations makes an infinitesimally small contribution to the process as a whole. This limit can be modeled using the differential equations that govern Brownian motion. The new boosting algorithm, named BrownBoost, is based on finding solutions to these differential equations.The paper describes two methods for finding approximate solutions to the differential equations. The first is a method that results in a provably polynomial time algorithm. The second method, based on the Newton-Raphson minimization procedure, is much more efficient in practice but is not known to be polynomial.