Abstract
Heuristic methods for the rejection of noisy training examples in the support vector machine (SVM) are introduced. Rejection of training errors, either offline or online, rsults in a sparser model that is less affected by noisy data. A simple offline heuristic provides sparser models with similar generalization performance to the standard SVM, at the expense of longer training times. An online approximation of this heuristic reduces training time and provides a sparser model than the SVM with a slight decrease in generalization performance.
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© 2001 Springer-Verlag Berlin Heidelberg
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Burbidge, R., Trotter, M., Buxton, B., Holden, S. (2001). STAR - Sparsity through Automated Rejection. In: Mira, J., Prieto, A. (eds) Connectionist Models of Neurons, Learning Processes, and Artificial Intelligence. IWANN 2001. Lecture Notes in Computer Science, vol 2084. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45720-8_78
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DOI: https://doi.org/10.1007/3-540-45720-8_78
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