Object Classification Using Support Vector Machines with Kernel-based Data Preprocessing

• Autorzy: Adamiak K. , Duch P. , Ślot K.

Identyfikatory

DOI

10.1515/ipc-2016-0015

• Języki publikacji: EN

Abstrakty

EN

The paper explores possibility of improving Support Vector Machine-based classification performance by introducing an input data dimensionality reduction step. Feature extraction by means of two different kernel methods are considered: kernel Principal Component Analysis (kPCA) and Supervised kernel Principal Component Analysis. It is hypothesized that input domain transformation, aimed at emphasizing between-class differences, would facilitate classification problem. Experiments, performed on three different datasets show that one can benefit from the proposed approach, as it provides lower variability in classification performance at similar, high recognition rates.

Słowa kluczowe

EN

SVM classification

• Wydawca: Instytut Telekomunikacji i Informatyki Uniwersytetu Technologiczno-Przyrodniczego w Bydgoszczy
• Czasopismo: Image Processing & Communications
• Rocznik: 2016
• Tom: Vol. 21, no. 3
• Strony: 45--54
• Opis fizyczny: Bibliogr. 17 poz., rys.

Twórcy

autor

Adamiak K.

• Institute of Applied Computer Science, Lodz University of Technology

autor

Duch P.

• Institute of Applied Computer Science, Lodz University of Technology

autor

Ślot K.

• Institute of Applied Computer Science, Lodz University of Technology

Bibliografia

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• [4] Chapelle, O., Vapnik, V., Bousquet, O., Mukherjee, S. (2002). Choosing multiple parameters for support vector machines. Machine learning, 46(1-3), 131-159
• [5] Cristianini, N., Shawe-Taylor, J. (2000). An introduction to support vector machines (and other kernel-based learning methods). Cambridge University Press
• [6] Dalal, N., Triggs, B. (2005, June). Histograms of oriented gradients for human detection. In Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on (Vol. 1, pp. 886-893). IEEE
• [7] Hofmann, T., Schölkopf, B., Smola, A.J. (2008). Kernel methods in machine learning. The annals of statistics, 1171-1220
• [8] Kim, K.I., Jung, K., Park, S.H., Kim, H.J. (2002). Support vector machines for texture classification. IEEE transactions on pattern analysis and machine intelligence, 24(11), 1542-1550
• [9] Lichman, M. (2013). UCI Machine Learning Repository http://archive.ics.uci.edu/ml. Irvine, CA: University of California. School of Information and Computer Science, 213
• [10] Mika, S., Ratsch, G., Weston, J., Schölkopf, B., Müllers, K. R. (1999, August). Fisher discriminant analysis with kernels. In Neural Networks for Signal Processing IX, 1999. Proceedings of the 1999 IEEE Signal Processing Society Workshop. (pp. 41-48). IEEE
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• [12] Müller, K. R., Smola, A., Rätsch, G., Schölkopf, B., Kohlmorgen, J., Vapnik, V. (1999). Using support vector machines for time series prediction. Advances in kernel methods-support vector learning, 243-254
• [13] Rangwala, H., Karypis, G. (2005). Profile-based direct kernels for remote homology detection and fold recognition. Bioinformatics, 21(23), 4239-4247
• [14] Schölkopf, B., Smola, A.J. (2002). Learning with Kernels. MIT Press, Cambridge, MA
• [15] Schölkopf, B., Smola, A., Müller, K.R. (1998). Nonlinear component analysis as a kernel eigenvalue problem. Neural computation, 10(5), 1299-1319
• [16] Song, L., Smola, A., Gretton, A., Bedo, J., Borgwardt, K. (2012). Feature selection via dependence maximization. Journal of Machine Learning Research, 13(May), 1393-1434
• [17] Wang, M., Sha, F., Jordan, M. I. (2010). Unsupervised kernel dimension reduction. In Advances in Neural Information Processing Systems (pp. 2379-2387)

Uwagi

PL

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).