Abstract
We introduce an approach based on perturbation method for input dimension reduction in Support Vector Machine (SVM) classifiers. If there exists redundant data components in training data set, they can be discarded by analyzing the total disturbance of the SVM output corresponding to the perturbed inputs. Thus, input dimension size is reduced and network becomes smaller. Algorithm for input dimension reduction is first formulated and then applied to real electroencephalography (EEG) data for recognition of epileptiform patterns.
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References
Vapnik, V.: Statistical Learning Theory. John Wiley, NY (1998)
Vapnik, V.: The support vector method of function estimation. In: Suykens, J.A.K., Vandewalle, J. (eds.) Nonlinear Modelling: Advanced Black-Box Techniques, pp. 55–85. Kluwer Academic Publishers, Boston (1998)
Acır, N., Öztura, İ., Kuntalp, M., Baklan, B., Güzeliş, C.: Automatic spike detection in EEG by a two stage procedure based on ANNs. In: Proceeding of International Conference on Artificial Neural Networks and Neural Information Processing, Istanbul, pp. 445–448 (2003)
Devijver, P.A., Kittler, J.: Pattern Recognition: A Statistical Approach. Prentice-Hall, Englewood Cliffs (1982)
Steppe, J.M., Bauer Jr, K.W.: Feature Sensitivity Measures. Computers Math. Applic. 23(2), 109–126 (1997)
Belue, L.M., Bauer Jr, K.W.: Determining input features for multilayer perceptrons. Neurocomputing 7, 111–121 (1995)
Zurada, J.M., Malinowski, A., Usui, S.: Perturbation Method for deleting redundant inputs of perceptron networks. Neurocomputing 14, 177–193 (1997)
Haykin, S.: Neural Networks: A comprehensive foundation. Prentice Hall, New Jersey (1999)
Bertsekas, D.P.: Nonlinear Programming. Athenas Scientific, Belmont (1995)
Chatrian, E., Bergamini, L., Dondey, M., Klass, D.W., Lennox-Buchthal, M., Petersen, I.: A glossary of terms most commonly used by clinical electroencephalographers. Electroencephalogr. Clin. Neurophysiol. 37, 538–548 (1974)
Kalaycı, T., Özdamar, Ö.: Wavelet preprocessing for automated neural network detection of EEG spikes. IEEE Eng. Med. Biol. 14(2), 160–166 (1995)
McNeil, B.J., Keeler, E., Adelstein, J.: Primer on certain elements of medical decision making. Journal of medicine (The New England) 293(5), 211–215 (1975)
Daubechies, I.: Ten lectures on wavelets. Capital city press, Montpelier (1992)
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Acır, N., Güzeliş, C. (2004). An Application of Support Vector Machine in Bioinformatics: Automated Recognition of Epileptiform Patterns in EEG Using SVM Classifier Designed by a Perturbation Method. In: Yakhno, T. (eds) Advances in Information Systems. ADVIS 2004. Lecture Notes in Computer Science, vol 3261. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30198-1_47
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DOI: https://doi.org/10.1007/978-3-540-30198-1_47
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23478-4
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