An expert system for mapping acoustic cues into phonetic features
Information Sciences: an International Journal
Automated Concept Acquisition in Noisy Environments
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine learning: an integrated framework and its applications
Machine learning: an integrated framework and its applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
C4.5: programs for machine learning
C4.5: programs for machine learning
Knowledge-based artificial neural networks
Artificial Intelligence
Machine Learning
Integrating Multiple Learning Strategies in First Order Logics
Machine Learning - Special issue on multistrategy 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
Refining Numerical Constants in First Order Logic Theories
Machine Learning - Special issue on multistrategy learning
Learning Logical Definitions from Relations
Machine Learning
An Experimental Evaluation of Coevolutive Concept Learning
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
ALT '96 Proceedings of the 7th International Workshop on Algorithmic Learning Theory
Reducing multiclass to binary: a unifying approach for margin classifiers
The Journal of Machine Learning Research
Search-intensive concept induction
Evolutionary Computation
Tractable induction and classification in first order logic via stochastic matching
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
SMART+: a multi-strategy learning tool
IJCAI'93 Proceedings of the 13th international joint conference on Artifical intelligence - Volume 2
An experimental study of phase transitions in matching
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Improvements to adaboost dynamic
Canadian AI'12 Proceedings of the 25th Canadian conference on Advances in Artificial Intelligence
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Boosting is a powerful and thoroughly investigated learning technique that improves the accuracy of any given learning algorithm by weighting training examples and hypotheses. Several authors contributed to the general boosting learning framework with theoretical and experimental results, mainly in the propositional learning framework. In a previous paper, we investigated the applicability of Freund and Schapire's AdaBoost.M1 algorithm to a first order logic weak learner. In this paper, we extend the weak learner in order to directly deal with weighted instances and compare two ways to apply boosting to such a weak learner: resampling instances at each round and using weighted instances.