Abstract
The kernel-based methodology of SVMs [Vapnik and Chervonenkis, 1974], [Vapnik, 1995a] has been established as a top ranking approach for supervised learning within both the theoretical and red practical research environments. This very performing technique suffers nevertheless from the curse of an opaque engine [Huysmans et al, 2006], which is undesirable for both theoreticians, who are keen to control the modeling, and the practitioners, who are more than often suspicious of using the prediction results as a reliable assistant in decision making.
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© 2014 Springer International Publishing Switzerland
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Stoean, C., Stoean, R. (2014). Support Vector Learning and Optimization. In: Support Vector Machines and Evolutionary Algorithms for Classification. Intelligent Systems Reference Library, vol 69. Springer, Cham. https://doi.org/10.1007/978-3-319-06941-8_2
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DOI: https://doi.org/10.1007/978-3-319-06941-8_2
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-06940-1
Online ISBN: 978-3-319-06941-8
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