The Strength of Weak Learnability
Machine Learning
Boosting a weak learning algorithm by majority
Information and Computation
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
BoosTexter: A Boosting-based Systemfor Text Categorization
Machine Learning - Special issue on information retrieval
Reducing multiclass to binary: a unifying approach for margin classifiers
The Journal of Machine Learning Research
Statistical Analysis of Some Multi-Category Large Margin Classification Methods
The Journal of Machine Learning Research
On the Consistency of Multiclass Classification Methods
The Journal of Machine Learning Research
Subset ranking using regression
COLT'06 Proceedings of the 19th annual conference on Learning Theory
Boosting with structure information in the functional space: an application to graph classification
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Boosting for transfer learning from multiple data sources
Pattern Recognition Letters
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We propose abc-boost (adaptive base class boost) for multi-class classification and present abc-mart, an implementation of abc-boost, based on the multinomial logit model. The key idea is that, at each boosting iteration, we adaptively and greedily choose a base class. Our experiments on public datasets demonstrate the improvement of abc-mart over the original mart algorithm.