Abstract
A new view of majority voting as a Monte Carlo stochastic algorithm is presented in this paper. Relation between the two approaches allows Adaboost’s example weighting strategy to be compared with the greedy covering strategy used for a long time in Machine Learning. The greedy covering strategy does not clearly show overfitting, it runs in at least one order of magnitude less time, it reaches zero error on the training set in few trials, and the error on the test set is most of the time comparable to that exhibited by AdaBoost.
Keywords
- Problem Instance
- Majority Vote
- Monte Carlo Algorithm
- Ensemble Learner
- Frequent Answer
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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© 2002 Springer-Verlag Berlin Heidelberg
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Esposito, R., Saitta, L. (2002). Is a Greedy Covering Strategy an Extreme Boosting?. In: Hacid, MS., Raś, Z.W., Zighed, D.A., Kodratoff, Y. (eds) Foundations of Intelligent Systems. ISMIS 2002. Lecture Notes in Computer Science(), vol 2366. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48050-1_12
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DOI: https://doi.org/10.1007/3-540-48050-1_12
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