Abstract
The use of ensemble methods for pattern classification have gained attention in recent years mainly due to its improvements on classification rates. This paper evaluates ensemble learning methods using the Minimal Learning Machines (MLM), a recently proposed supervised learning algorithm. Additionally, we introduce an alternative output estimation procedure to reduce the complexity of the standard MLM. The proposed methods are evaluated on real datasets and compared to several state-of-the-art classification algorithms.
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Mesquita, D.P.P., Gomes, J.P.P., Junior, A.H.S. (2015). Ensemble of Minimal Learning Machines for Pattern Classification. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2015. Lecture Notes in Computer Science(), vol 9095. Springer, Cham. https://doi.org/10.1007/978-3-319-19222-2_12
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DOI: https://doi.org/10.1007/978-3-319-19222-2_12
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