An empirical comparison of different approaches for combining multimodal neuroimaging data with support vector machine.

ABSTRACT: In the pursuit of clinical utility, neuroimaging researchers of psychiatric and neurological illness are increasingly using analyses, such as support vector machine, that allow inference at the single-subject level. Recent studies employing single-modality data, however, suggest that classification accuracies must be improved for such utility to be realized. One possible solution is to integrate different data types to provide a single combined output classification; either by generating a single decision function based on an integrated kernel matrix, or, by creating an ensemble of multiple single modality classifiers and integrating their predictions. Here, we describe four integrative approaches: (1) an un-weighted sum of kernels, (2) multi-kernel learning, (3) prediction averaging, and

SUBMITTER: Pettersson-Yeo W 

PROVIDER: S-EPMC4097812 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature