Abstract
The hybridization of classifiers can lead to significant improvements of learning results. In this chapter, we introduce an ensemble of K-nearest neighbor and SVM classifiers and analyze its performance in a real-world application [69]. The ensembles are hybrids of local nearest neighbors classifiers that are based on averaging labels in the neighborhood of unknown patterns and the global SVMs that use separating hyperplanes.
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© 2013 Springer-Verlag Berlin Heidelberg
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Kramer, O. (2013). Ensemble Learning. In: Dimensionality Reduction with Unsupervised Nearest Neighbors. Intelligent Systems Reference Library, vol 51. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38652-7_3
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DOI: https://doi.org/10.1007/978-3-642-38652-7_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38651-0
Online ISBN: 978-3-642-38652-7
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