Abstract
We propose an innovative learning algorithm for a support vector machine to be robust. As learning patterns it uses not only the prescribed learning patterns but also their neighbour patterns. The size of the proposed optimization problem to be solved is the same as the original one. Many simulations show the effectiveness of the proposed algorithm.
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Guo, J., Takahashi, N., Nishi, T. (2004). A Learning Method for Robust Support Vector Machines. In: Yin, FL., Wang, J., Guo, C. (eds) Advances in Neural Networks – ISNN 2004. ISNN 2004. Lecture Notes in Computer Science, vol 3173. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28647-9_79
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DOI: https://doi.org/10.1007/978-3-540-28647-9_79
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22841-7
Online ISBN: 978-3-540-28647-9
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