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Titlebook: Computer Networks and Intelligent Computing; 5th International Co K. R. Venugopal,L. M. Patnaik Conference proceedings 2011 Springer-Verlag

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Secondary Ion Mass Spectroscopy (SIMS)ries are diverse. The proposed ensembles are simple but quite accurate. We study the performance of the proposed ensembles against the other popular ensemble techniques. The proposed ensemble method matches or outperforms Bagging, and is competitive with Adaboost.M1 and Random Forests.
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https://doi.org/10.1007/978-3-540-71355-5takes care of .-anonymity and .-diversity to a considerable extent and in combination with the improved second and third phases of the algorithm in [5] leads to an efficient .-diversity algorithm. We also show that all the three stages of the algorithm are necessary in order to cover different situations.
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A Novel Ensemble Method for Time Series Classificationries are diverse. The proposed ensembles are simple but quite accurate. We study the performance of the proposed ensembles against the other popular ensemble techniques. The proposed ensemble method matches or outperforms Bagging, and is competitive with Adaboost.M1 and Random Forests.
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An Improved ,-Diversity Anonymisation Algorithmtakes care of .-anonymity and .-diversity to a considerable extent and in combination with the improved second and third phases of the algorithm in [5] leads to an efficient .-diversity algorithm. We also show that all the three stages of the algorithm are necessary in order to cover different situations.
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