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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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
Online ISBN: 978-3-540-30198-1
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