Abstract
Parameter selection greatly impacts the classification accuracy of Support Vector Machines (SVM). However, this step is often overlooked in experimental comparisons, for it is time consuming and requires familiarity with the inner workings of SVM. Focusing on Gaussian RBF kernels, we propose a grid-search procedure for SVM parameter selection which is economic in its running time and does not require user intervention. Based on probabilistic assumptions of standardized data, this procedure works by filtering out parameter values that are not likely to yield reasonable classification accuracy. We instantiate this procedure in the popular WEKA data mining toolbox and show its performance on real datasets.
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References
Cortes, C., Vapnik, V.: Support vector networks. Machine Learning 20(3), 273–297 (1995)
Hsu, C.W., Chang, C.C., Lin, C.J.: A practical guide to support vector classification. Technical report (2003), http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf
Ben-Hur, A., Weston, J.: A user’s guide to support vector machines. Methods in Molecular Biology 609, 223–239 (2010)
Witten, I.H., Eibe, F., Hall, M.H.: Data Mining: Practical Machine Learning Tools and Techniques, 3rd edn. Morgan Kaufmann Publishers Inc. (2010)
Burges, C.J.C.: A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery 2(2), 121–167 (1998)
Vapnik, V.: Statistical Learning Theory. Adaptive and Learning Systems for Signal Processing, Communications, and Control. Wiley-Interscience (1998)
Steinwart, I.: On the influence of the kernel on the consistency of support vector machines. Journal of Machine Learning Research 2, 67–93 (2001)
Chang, C.C., Lin, C.J.: LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology
Collobert, R., Bengio, S.: SVMTorch: Support vector machines for large-scale regression problems. Journal of Machine Learning Research 1, 143–160 (2001)
Bache, K., Lichman, M.: UCI machine learning repository (2013)
Platt, J.C.: Fast training of support vector machines using sequential minimal optimization. MIT Press (1999)
Fawcett, T.: An introduction to ROC analysis. Pattern Recognition Letters 27(8), 861–874 (2006)
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Braga, I., do Carmo, L.P., Benatti, C.C., Monard, M.C. (2013). A Note on Parameter Selection for Support Vector Machines. In: Castro, F., Gelbukh, A., González, M. (eds) Advances in Soft Computing and Its Applications. MICAI 2013. Lecture Notes in Computer Science(), vol 8266. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-45111-9_21
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DOI: https://doi.org/10.1007/978-3-642-45111-9_21
Publisher Name: Springer, Berlin, Heidelberg
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