Abstract
Support vector machines have recently attracted much attention in the machine learning and optimization communities for their remarkable generalization ability. An open problem, however, is the selection of the optimal kernel matrix for regression problems. Recently, a means to compute the optimal kernel matrix for pattern classification using semidefinite programming has been introduced [7]. In this paper we extend these thoughts to the regression analysis scenario. Preliminary experimental results are presented for which the optimal kernel matrix for support vector machine regression is retrieved.
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Trafalis, T.B., Malyscheff, A.M. (2004). Optimal Selection of the Regression Kernel Matrix with Semidefinite Programming. In: Floudas, C.A., Pardalos, P. (eds) Frontiers in Global Optimization. Nonconvex Optimization and Its Applications, vol 74. Springer, Boston, MA. https://doi.org/10.1007/978-1-4613-0251-3_31
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DOI: https://doi.org/10.1007/978-1-4613-0251-3_31
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4613-7961-4
Online ISBN: 978-1-4613-0251-3
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