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Applicational aspects of support vector machines
The special emphasis of support vector machines (SVMs) on generalization ability makes this approach particularly interesting for real‐world applications with limited amounts of training data. In this paper we analyse the applicational aspects of SVMs, illustrating them with the step‐by‐step constru...
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Published in: | Journal of chemometrics 2002-08, Vol.16 (8-10), p.482-489 |
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cites | cdi_FETCH-LOGICAL-c3934-dd7fc6a244a5db7dd0acc726a163cf641a331013829cd4c3152f6d286f85bff13 |
container_end_page | 489 |
container_issue | 8-10 |
container_start_page | 482 |
container_title | Journal of chemometrics |
container_volume | 16 |
creator | Belousov, A. I. Verzakov, S. A. von Frese, J. |
description | The special emphasis of support vector machines (SVMs) on generalization ability makes this approach particularly interesting for real‐world applications with limited amounts of training data. In this paper we analyse the applicational aspects of SVMs, illustrating them with the step‐by‐step construction of a classifier for polymers by means of their mid‐infrared spectra. With this example we show how the main difficulties of a typical industrial classification task can be addressed using SVMs. Copyright © 2002 John Wiley & Sons, Ltd. |
doi_str_mv | 10.1002/cem.744 |
format | article |
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subjects | Chemistry classification Exact sciences and technology General and physical chemistry General. Nomenclature, chemical documentation, computer chemistry mid-infrared spectroscopy polymer recognition support vector machines Theory of reactions, general kinetics. Catalysis. Nomenclature, chemical documentation, computer chemistry |
title | Applicational aspects of support vector machines |
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