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A Modified SVM Classification Algorithm for Data of Variable Quality

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Knowledge-Based Intelligent Information and Engineering Systems (KES 2007)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4694))

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Abstract

We propose a modified SVM algorithm for the classification of data augmented with explicit quality quantification for each example in the training set. As the extension to nonlinear decision functions through the use of kernels brings to a non-convex optimization problem, we develop an approximate solution. Finally, the proposed approach is applied to a set of benchmarks and contrasted with analogous methodologies in the literature.

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References

  1. Apolloni, B., Malchiodi, D.: Embedding sample points relevance in svm linear classification. In: Torra, V., Narukawa, Y., Valls, A., Domingo-Ferrer, J. (eds.) MDAI 2006. LNCS (LNAI), vol. 3885, Springer, Heidelberg (2006)

    Google Scholar 

  2. Cortes, C., Vapnik, V.: Support-vector networks. Machine Learning 20, 121–167 (1995)

    Google Scholar 

  3. Fletcher, R.: Practical Methods of Optimisations, 2nd edn. John Wiley & Sons, Chichester (1987)

    Google Scholar 

  4. Malchiodi, D.: Embedding sample point uncertainty measures in learning algorithms. Nonlinear Analysis: Hybrid Systems (in press) (doi:10.1016/j.nahs.2006.12.004)

    Google Scholar 

  5. Schölkopf, B., Smola, A.J.: Learning with kernels: support vector machines, regularization, optimization, and beyond. MIT Press, Cambridge, MA (2002)

    Google Scholar 

  6. Theodoridis, S., Koutroumbas, K.: Pattern recognition. Elsevier/Academic Press, Amsterdam, Boston (2006)

    Google Scholar 

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Bruno Apolloni Robert J. Howlett Lakhmi Jain

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© 2007 Springer-Verlag Berlin Heidelberg

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Apolloni, B., Malchiodi, D., Natali, L. (2007). A Modified SVM Classification Algorithm for Data of Variable Quality. In: Apolloni, B., Howlett, R.J., Jain, L. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2007. Lecture Notes in Computer Science(), vol 4694. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74829-8_17

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  • DOI: https://doi.org/10.1007/978-3-540-74829-8_17

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-74828-1

  • Online ISBN: 978-3-540-74829-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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