Abstract
In this chapter we deal with the use of Support Vector Machines in gas sensing. After a brief introduction to the inner workings of multisensor systems, the potential benefits of SVMs in this type of instruments are discussed. Examples on how SVMs are being evaluated in the gas sensor community are described in detail, including studies in their generalisation ability, their role as a valid variable selection technique and their regression performance. These studies have been carried out measuring different blends of coffee, different types of vapours (CO, O2, acetone, hexanal, etc.) and even discriminating between different types of nerve agents.
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Brezmes, J., Llobet, E., Al-Khalifa, S., Maldonado, S., Gardner, J. Gas Sensing Using Support Vector Machines. In: Wang, L. (eds) Support Vector Machines: Theory and Applications. Studies in Fuzziness and Soft Computing, vol 177. Springer, Berlin, Heidelberg. https://doi.org/10.1007/10984697_17
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DOI: https://doi.org/10.1007/10984697_17
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Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-24388-5
Online ISBN: 978-3-540-32384-6
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