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cs.LG2019
A Comparative Analysis of XGBoost
Candice Bentéjac, Anna Csörgő, Gonzalo Martínez-Muñoz
XGBoost is a scalable ensemble technique based on gradient boosting that has demonstrated to be a reliable and efficient machine learning challenge solver. This work proposes a pra…
cs.LG2018
Pooling homogeneous ensembles to build heterogeneous ones
Maryam Sabzevari, Gonzalo Martínez-Muñoz, Alberto Suárez
In ensemble methods, the outputs of a collection of diverse classifiers are combined in the expectation that the global prediction be more accurate than the individual ones. Hetero…
cs.LG2016
Vote-boosting ensembles
Maryam Sabzevari, Gonzalo Martínez-Muñoz, Alberto Suárez
Vote-boosting is a sequential ensemble learning method in which the individual classifiers are built on different weighted versions of the training data. To build a new classifier,…