Communications of the ACM
What size net gives valid generalization?
Neural Computation
The Strength of Weak Learnability
Machine Learning
A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
C4.5: programs for machine learning
C4.5: programs for machine learning
Cryptographic limitations on learning Boolean formulae and finite automata
Journal of the ACM (JACM)
The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Boosting a weak learning algorithm by majority
Information and Computation
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
Improved boosting algorithms using confidence-rated predictions
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Boosting and Rocchio applied to text filtering
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Boosting in the limit: maximizing the margin of learned ensembles
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Training methods for adaptive boosting of neural networks
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
An adaptive version of the boost by majority algorithm
COLT '99 Proceedings of the twelfth annual conference on Computational learning theory
COLT '99 Proceedings of the twelfth annual conference on Computational learning theory
Using Decision Trees to Construct a Practical Parser
Machine Learning - Special issue on natural language learning
A simple, fast, and effective rule learner
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
BoosTexter: A Boosting-based Systemfor Text Categorization
Machine Learning - Special issue on information retrieval
Using output codes to boost multiclass learning problems
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
The Alternating Decision Tree Learning Algorithm
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
An Efficient Boosting Algorithm for Combining Preferences
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
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
IEEE Transactions on Information Theory
Scaling Boosting by Margin-Based Inclusionof Features and Relations
ECML '02 Proceedings of the 13th European Conference on Machine Learning
Relational Learning Using Constrained Confidence-Rated Boosting
ILP '01 Proceedings of the 11th International Conference on Inductive Logic Programming
Performance Degradation in Boosting
MCS '01 Proceedings of the Second International Workshop on Multiple Classifier Systems
COLT '01/EuroCOLT '01 Proceedings of the 14th Annual Conference on Computational Learning Theory and and 5th European Conference on Computational Learning Theory
Improved Statistical Techniques for Multi-part Face Detection and Recognition
SCIA '09 Proceedings of the 16th Scandinavian Conference on Image Analysis
Improving the accuracy of suicide attempter classification
Artificial Intelligence in Medicine
Text classification for data loss prevention
PETS'11 Proceedings of the 11th international conference on Privacy enhancing technologies
Learning to rank with nonlinear monotonic ensemble
MCS'11 Proceedings of the 10th international conference on Multiple classifier systems
Ensemble-approaches for clustering health status of oil sand pumps
Expert Systems with Applications: An International Journal
New methodology of computer aided diagnostic system on breast cancer
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part III
Positive semidefinite metric learning using boosting-like algorithms
The Journal of Machine Learning Research
Defect cluster recognition system for fabricated semiconductor wafers
Engineering Applications of Artificial Intelligence
Combining analytic kernel models for energy-efficient data modeling and classification
The Journal of Supercomputing
Early experiments with neural diversity machines
Neurocomputing
A novel online boosting algorithm for automatic anatomy detection
Machine Vision and Applications
Fully corrective boosting with arbitrary loss and regularization
Neural Networks
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Boosting is a general method for improving the accuracy of any given learning algorithm. Focusing primarily on the AdaBoost algorithm, we briefly survey theoretical work on boosting including analyses of AdaBoost's training error and generalization error, connections between boosting and game theory, methods of estimating probabilities using boosting, and extensions of AdaBoost for multiclass classiffication problems. Some empirical work and applications are also described.