Abstract
In this paper we combine Support Vector Machines and the ARTMAP architecture to build a statistically consistent classification procedure. This combination allows ARTMAP to process noisy data while avoiding category proliferation and sensitivity to pattern order presentation. In addition, the combination provides a natural approach and an alternative way to perform multiclass classification with SVMs. To conclude, results on some illustrative data sets are provided.
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Muñoz, A., Moguerza, J.M. (2003). Combining Support Vector Machines and ARTMAP Architectures for Natural Classification. In: Palade, V., Howlett, R.J., Jain, L. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2003. Lecture Notes in Computer Science(), vol 2774. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45226-3_3
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DOI: https://doi.org/10.1007/978-3-540-45226-3_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-40804-8
Online ISBN: 978-3-540-45226-3
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