The Strength of Weak Learnability
Machine Learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Efficient sampling strategies for relational database operations
ICDT Selected papers of the 4th international conference on Database theory
Query size estimation by adaptive sampling
Selected papers of the 9th annual ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
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
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
Efficient progressive sampling
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
From Computational Learning Theory to Discovery Science
ICAL '99 Proceedings of the 26th International Colloquium on Automata, Languages and Programming
Practical Algorithms for On-line Sampling
DS '98 Proceedings of the First International Conference on Discovery Science
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Sequential Sampling Algorithms: Unified Analysis and Lower Bounds
SAGA '01 Proceedings of the International Symposium on Stochastic Algorithms: Foundations and Applications
How Can Computer Science Contribute to Knowledge Discovery?
SOFSEM '01 Proceedings of the 28th Conference on Current Trends in Theory and Practice of Informatics Piestany: Theory and Practice of Informatics
Sequential Sampling Techniques for Algorithmic Learning Theory
ALT '00 Proceedings of the 11th International Conference on Algorithmic Learning Theory
Algorithmic Aspects of Boosting
Progress in Discovery Science, Final Report of the Japanese Discovery Science Project
A new method for adaptive sequential sampling for learning and parameter estimation
ISMIS'11 Proceedings of the 19th international conference on Foundations of intelligent systems
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In this paper we present a experimental evaluation of a boosting based learning system and show that can be run efficiently over a large dataset. The system uses as base learner decision stumps, single atribute decision trees with only two terminal nodes. To select the best decision stump at each iteration we use an adaptive sampling method. As a boosting algorithm, we use a modification of AdaBoost that is suitable to be combined with a base learner that does not use all the dataset. We provide experimental evidence that our method is as accurate as the equivalent algorithm that uses all the dataset but much faster.