Comparative Study of SVM Methods Combined with Voxel Selection for Object Category Classification on fMRI Data
Sutao Song; Zhichao Zhan; Zhiying Long; Jiacai Zhang; Yao Li · 2011 · PLoS ONE
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract
BACKGROUND: Support vector machine (SVM) has been widely used as accurate and reliable method to decipher brain patterns from functional MRI (fMRI) data. Previous studies have not found a clear benefit for non-linear (polynomial kernel) SVM versus linear one. Here, a more effective non-linear SVM using radial basis function (RBF) kernel is compared with linear SVM. Different from traditional studies which focused either merely on the evaluation of different types of SVM or the voxel selection methods, we aimed to investigate the overall performance of linear and RBF SVM for fMRI classification
Abstract by Sutao Song; Zhichao Zhan; Zhiying Long; Jiacai Zhang; Yao Li, PLoS ONE (2011) — licensed CC BY 4.0.
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Metadata source: OpenAlex · DOI 10.1371/journal.pone.0017191