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dc.contributor.authorZhou, L.-
dc.contributor.authorWang, L.-
dc.contributor.authorLiu, L.-
dc.contributor.authorOgunbona, P.-
dc.contributor.authorShen, D.-
dc.identifier.citationLecture Notes in Artificial Intelligence, 2014, vol.17, iss.Part III, pp.321-328-
dc.description.abstractRecently, neuroimaging data have been increasingly used to study the causal relationship among brain regions for the understanding and diagnosis of brain diseases. Recent work on sparse Gaussian Bayesian network (SGBN) has shown it as an efficient tool to learn large scale directional brain networks from neuroimaging data. In this paper, we propose a learning approach to constructing SGBNs that are both representative and discriminative for groups in comparison. A maxmargin criterion built directly upon the SGBN models is proposed to effectively optimize the classification performance of the SGBNs. The proposed method shows significant improvements over the state-of-theart works in the discriminative power of SGBNs.-
dc.description.statementofresponsibilityLuping Zhou, Lei Wang, Lingqiao Liu, Philip Ogunbona, and Dinggang Shen-
dc.relation.ispartofseriesLecture Notes in Computer Science (LNCS, vol. 8675)-
dc.rights© Springer International Publishing Switzerland 2014-
dc.subjectNerve Net-
dc.subjectAlzheimer Disease-
dc.subjectImage Interpretation, Computer-Assisted-
dc.subjectRadionuclide Imaging-
dc.subjectDiscriminant Analysis-
dc.subjectBayes Theorem-
dc.subjectSensitivity and Specificity-
dc.subjectReproducibility of Results-
dc.subjectArtificial Intelligence-
dc.subjectPattern Recognition, Automated-
dc.titleMax-margin based learning for discriminative Bayesian network from neuroimaging data-
dc.typeConference paper-
dc.contributor.conference17th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2014) (14 Sep 2014 - 18 Sep 2014 : Boston, MA, USA)-
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