Learning using structured data: Application to fMRI data analysis

Lichen Liang, Vladimir Cherkassky

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Scopus citations

Abstract

This paper investigates a new learning setting recently introduced by Vapnik [8] that takes into account a known structure of the training data to improve generalization performance. This setting is a special case of a new inference technology known as Learning with Hidden Information [8] suitable for many real-life applications with sparse high-dimensional data. We first briefly describe an extension of SVM called SVMγ+ [8] that is associated with this new learning setting, and verify its effectiveness using a synthetic data set. Then we demonstrate the effectiveness of SVMγ+ on a difficult real-life problem: detection of cognitive states from fMRI images obtained from different subjects. These empirical results show that the SVMγ+ approach achieves improved inter-subject generalization vs standard SVM technology.

Original languageEnglish (US)
Title of host publicationThe 2007 International Joint Conference on Neural Networks, IJCNN 2007 Conference Proceedings
Pages495-499
Number of pages5
DOIs
StatePublished - Dec 1 2007
Event2007 International Joint Conference on Neural Networks, IJCNN 2007 - Orlando, FL, United States
Duration: Aug 12 2007Aug 17 2007

Publication series

NameIEEE International Conference on Neural Networks - Conference Proceedings
ISSN (Print)1098-7576

Other

Other2007 International Joint Conference on Neural Networks, IJCNN 2007
Country/TerritoryUnited States
CityOrlando, FL
Period8/12/078/17/07

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