Titlebook: Ensembles in Machine Learning Applications; Oleg Okun,Giorgio Valentini,Matteo Re Book 2011 Springer Berlin Heidelberg 2011 Computational

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Original

Minimally-Sized Balanced Decomposition Schemes for Multi-class Classification,les. Therefore we propose voting with MBDS ensembles (VMBDSs).We show that the generalization performance of the VMBDSs ensembles improves with the number of MBDS classifiers. However this number can become large and thus the VMBDSs ensembles can have a computational-complexity problem as well. Fort

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An Improved Mixture of Experts Model: Divide and Conquer Using Random Prototypes,testing strategies of the standard HME model are also modified, based on the same insight applied to standard ME. In both cases, the proposed approach does not require to train the gating networks, as they are implemented with simple distance-based rules. In so doing the overall time required for tr

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Palter

https://doi.org/10.1007/978-3-030-76107-3les. Therefore we propose voting with MBDS ensembles (VMBDSs).We show that the generalization performance of the VMBDSs ensembles improves with the number of MBDS classifiers. However this number can become large and thus the VMBDSs ensembles can have a computational-complexity problem as well. Fort

Palter

https://doi.org/10.1007/978-3-319-41585-7rformances are obtained with the semi-supervised data-driven network. However, combining it with the expertise-driven network improves performance in many cases and leads to interesting insights about the datasets, networks and metrics.

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https://doi.org/10.1007/978-3-319-57306-9ection to maximize the amount of validation data considering, in turn, each fold as a validation fold to select the trees from. The aim is to increase performance by reducing the variance of the tree ensemble selection process. We demonstrate the effectiveness of our approach on several UCI and real-world data sets.