Abstract
This paper analyzes the use of ARTMAP-based in structures of ensembles designed by three variants of boosting (Aggressive, Conservative and Inverse). In this investigation, it is aimed to analyze the influence of the RePART (Reward and Punishment ARTmap) neural network in ARTMAP-based ensembles, intending to define whether the use of this model is positive for ARTMAP-based ensembles. In addition, it aims to define which boosting strategy is the most suitable to be used in ARTMAP-based ensembles.
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de Medeiros Santos, A., de Paula Canuto, A.M. (2008). Using ARTMAP-Based Ensemble Systems Designed by Three Variants of Boosting. In: Kůrková, V., Neruda, R., KoutnÃk, J. (eds) Artificial Neural Networks - ICANN 2008. ICANN 2008. Lecture Notes in Computer Science, vol 5163. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87536-9_58
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DOI: https://doi.org/10.1007/978-3-540-87536-9_58
Publisher Name: Springer, Berlin, Heidelberg
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