An application of support vector machine in bioinformatics: Automated recognition of epileptiform patterns in EEG using SVM classifier designed by a perturbation method

Acir, N; Guzelis, C

doi:10.1007/978-3-540-30198-1_47

An application of support vector machine in bioinformatics: Automated recognition of epileptiform patterns in EEG using SVM classifier designed by a perturbation method

Atıf İçin Kopyala

Acir N., Guzelis C.

ADVANCES IN INFORMATION SYSTEMS, PROCEEDINGS, cilt.3261, ss.462-471, 2004 (SCI-Expanded)

Yayın Türü: Makale / Tam Makale
Cilt numarası: 3261
Basım Tarihi: 2004
Doi Numarası: 10.1007/978-3-540-30198-1_47
Dergi Adı: ADVANCES IN INFORMATION SYSTEMS, PROCEEDINGS
Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
Sayfa Sayıları: ss.462-471
Dokuz Eylül Üniversitesi Adresli: Hayır

Özet

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.