http://www.nitrc.org/forum/forum.php?forum_id=6812 <table border="0" width="100%"><tr><td align="left"/></tr></table> <p><b>Pairwise Classifier Ensemble with Adaptive Sub-Classifiers for fMRI Pattern Analysis.</b></p> <p>Neurosci Bull. 2016 Nov 12;</p> <p>Authors: Kim E, Park H</p> <p>Abstract<br/> The multi-voxel pattern analysis technique is applied to fMRI data for classification of high-level brain functions using pattern information distributed over multiple voxels. In this paper, we propose a classifier ensemble for multiclass classification in fMRI analysis, exploiting the fact that specific neighboring voxels can contain spatial pattern information. The proposed method converts the multiclass classification to a pairwise classifier ensemble, and each pairwise classifier consists of multiple sub-classifiers using an adaptive feature set for each class-pair. Simulated and real fMRI data were used to verify the proposed method. Intra- and inter-subject analyses were performed to compare the proposed method with several well-known classifiers, including single and ensemble classifiers. The comparison results showed that the proposed method can be generally applied to multiclass classification in both simulations and real fMRI analyses.<br/> </p><p>PMID: 27838826 [PubMed - as supplied by publisher]</p> en-us Copyright 2000-2026 NITRC OSI Wed, 06 May 2026 3:59:25 GMT http://blogs.law.harvard.edu/tech/rss NITRC RSS generator