Abstract
Based on studies and experiments on the loss term of SVMs, we argue that 1-norm measurement is better than 2-norm measurement for outlier resistance. Thus, we modify the previous 2-norm soft margin smooth support vector machine (SSVM2) to propose a new 1-norm soft margin smooth support vector machine (SSVM1). Both SSVMs can be solved in primal form without a sophisticated optimization solver. We also propose a heuristic method for outlier filtering which costs little in training process and improves the ability of outlier resistance a lot. The experimental results show that SSVM1 with outlier filtering heuristic performs well not only on the clean, but also the polluted synthetic and benchmark UCI datasets.
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Chien, LJ., Lee, YJ., Kao, ZP., Chang, CC. (2010). Robust 1-Norm Soft Margin Smooth Support Vector Machine. In: Fyfe, C., Tino, P., Charles, D., Garcia-Osorio, C., Yin, H. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2010. IDEAL 2010. Lecture Notes in Computer Science, vol 6283. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15381-5_18
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DOI: https://doi.org/10.1007/978-3-642-15381-5_18
Publisher Name: Springer, Berlin, Heidelberg
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