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Support Vector Machines for neuroimage analysis: Interpretation from discrimination

Chapter


Abstract


  • Support vector machines (SVMs) have been widely used in neuroimage analysis as an effective multivariate analysis tool for group comparison. As neuroimage analysis is often an exploratory research, it is an important issue to characterize the group difference captured by SVM with anatomically interpretable patterns, which provides insights into the unknown mechanism of the brain. In this chapter, SVM-based methods and pplications are introduced for neuroimage analyis from this point of view. The discriminative patterns are decoded from SVMs through distinctive feature selection, SVM decision boundary interpretation, and discriminative learning of generative models.

Publication Date


  • 2014

Citation


  • Zhou, L., Wang, L., Liu, L., Ogunbona, P. O. & Shen, D. (2014). Support Vector Machines for neuroimage analysis: Interpretation from discrimination. In Y. Ma & G. Guo (Eds.), Support Vector Machines Applications (pp. 191-220). Germany: Springer.

International Standard Book Number (isbn) 13


  • 9783319022994

Scopus Eid


  • 2-s2.0-84927745290

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/4120

Book Title


  • Support Vector Machines Applications

Has Global Citation Frequency


Start Page


  • 191

End Page


  • 220

Place Of Publication


  • Germany

Abstract


  • Support vector machines (SVMs) have been widely used in neuroimage analysis as an effective multivariate analysis tool for group comparison. As neuroimage analysis is often an exploratory research, it is an important issue to characterize the group difference captured by SVM with anatomically interpretable patterns, which provides insights into the unknown mechanism of the brain. In this chapter, SVM-based methods and pplications are introduced for neuroimage analyis from this point of view. The discriminative patterns are decoded from SVMs through distinctive feature selection, SVM decision boundary interpretation, and discriminative learning of generative models.

Publication Date


  • 2014

Citation


  • Zhou, L., Wang, L., Liu, L., Ogunbona, P. O. & Shen, D. (2014). Support Vector Machines for neuroimage analysis: Interpretation from discrimination. In Y. Ma & G. Guo (Eds.), Support Vector Machines Applications (pp. 191-220). Germany: Springer.

International Standard Book Number (isbn) 13


  • 9783319022994

Scopus Eid


  • 2-s2.0-84927745290

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/4120

Book Title


  • Support Vector Machines Applications

Has Global Citation Frequency


Start Page


  • 191

End Page


  • 220

Place Of Publication


  • Germany