Support vector machines for neuroimage analysis: Interpretation from discrimination

Luping Zhou, Lei Wang, Lingqiao Liu, Philip Ogunbona, Dinggang Shen

Research output: Chapter in Book/Report/Conference proceedingChapter

6 Citations (Scopus)

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 applications are introduced for neuroimage analysis 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.

Original languageEnglish
Title of host publicationSupport Vector Machines Applications
PublisherSpringer International Publishing
Pages191-220
Number of pages30
ISBN (Print)9783319023007, 3319022997, 9783319022994
DOIs
Publication statusPublished - 2013 Nov 1
Externally publishedYes

ASJC Scopus subject areas

  • Engineering(all)
  • Computer Science(all)

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  • Cite this

    Zhou, L., Wang, L., Liu, L., Ogunbona, P., & Shen, D. (2013). Support vector machines for neuroimage analysis: Interpretation from discrimination. In Support Vector Machines Applications (pp. 191-220). Springer International Publishing. https://doi.org/10.1007/978-3-319-02300-7_6