Classifying the multiplicity of the EEG source models using sphere-shaped support vector Machines

Support vector machines (SVMs) are learning algorithms derived from statistical learning theory, and originally designed to solve binary classification problems. How to effectively extend SVMs for multiclass classification problems is still an ongoing research issue. In this paper, a sphere-shaped S...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	IEEE transactions on magnetics 2005-05, Vol.41 (5), p.1912-1915
Hauptverfasser:	Qing Wu, Qing Wu, Xueqin Shen, Xueqin Shen, Ying Li, Ying Li, Guizhi Xu, Guizhi Xu, Weili Yan, Weili Yan, Guoya Dong, Guoya Dong, Qingxin Yang, Qingxin Yang
Format:	Artikel
Sprache:	eng
Schlagworte:	Brain modeling Computer science Cross-disciplinary physics: materials science rheology EEG source model Electroencephalography Electromagnetic fields Electromagnetic modeling Exact sciences and technology Magnetism Materials science multiclass classification Other topics in materials science Physics Predictive models Quadratic programming Scalp sphere classifier support vector machine Support vector machine classification Support vector machines
Online-Zugang:	Volltext bestellen
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!