Improved brain pattern recovery through ranking approaches

Proceedings - 2012 2nd International Workshop on Pattern Recognition in NeuroImaging, PRNI 2012

Volumn , Issue , 2012, Pages 9-12

  Pedregosa, Fabian ^a,b,d   Cauvet, Elodie ^b,c   Varoquaux, Gaël ^a,b   Pallier, Christophe ^a,b,c   Thirion, Bertrand ^a,b   Gramfort, Alexandre ^a,b  

^a INRIA SACLAY   (France)

^b DIF   (France)

^c INSERM   (France)

^d INRIA ROCQUENCOURT   (France)

Author keywords

decoding; fMRI; ranking; supervised learning

Indexed keywords

BRAIN REGIONS; DATA SETS; FMRI; FUNCTIONAL MAGNETIC RESONANCE IMAGES (FMRI); FUNCTIONAL SPECIFICITY; GENERAL LINEAR MODELS; INCORPORATING PRIOR KNOWLEDGE; LEAST SQUARE REGRESSION; PATTERN RECOVERY; RANKING; RANKING APPROACH; RECOVERY PROPERTIES; STATISTICAL EFFECTS; STATISTICAL PROBLEMS;

BRAIN; MAGNETIC RESONANCE IMAGING; MULTIVARIANT ANALYSIS; NEUROIMAGING; PATTERN RECOGNITION; PRASEODYMIUM ALLOYS; SUPERVISED LEARNING;

DECODING;

EID: 84867749878     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/PRNI.2012.23     Document Type: Conference Paper

References (10)

1. O. Yamashita, M. aki Sato, T. Yoshioka, F. Tong, and Y. Kamitani, "Sparse estimation automatically selects voxels relevant for the decoding of fMRI activity patterns," NeuroImage, vol. 42, no. 4, pp. 1414-1429, 2008.
2. S. Ryali, K. Supekar, D. A. Abrams, and V. Menon, "Sparse logistic regression for whole-brain classification of fmri data." NeuroImage, vol. 51, no. 2, pp. 752-764, 2010.
3. V. Michel, A. Gramfort, G. Varoquaux, E. Eger, and B. Thirion, "Total variation regularization for fMRI-based prediction of behaviour." IEEE Transactions on Medical Imaging, vol. 30, no. 7, pp. 1328-1340, Feb. 2011.
4. J. Mouro-Miranda, A. L. Bokde, C. Born, H. Hampel, and M. Stetter, "Classifying brain states and determining the discriminating activation patterns: Support vector machine on functional MRI data," NeuroImage, vol. 28, p. 980, 2005.
5. R. Herbrich, T. Graepel, and K. Obermayer, Large margin rank boundaries for ordinal regression. MIT Press, Cambridge, MA, 2000, vol. 88, pp. 115-132.
6. Y. Cao, J. Xu, T.-Y. Liu, H. Li, Y. Huang, and H.-W. Hon, "Adapting ranking svm to document retrieval," in Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval, ser. SIGIR '06. New York, NY, USA: ACM, 2006, pp. 186-193.
7. O. Dekel, C. Manning, and Y. Singer, "Log-linear models for label ranking," Advances in Neural Information Processing Systems, vol. 16, no. 2, p. 497504, 2003.
8. R. E. Fan, K. W. Chang, C. J. Hsieh, X. R. Wang, and C. J. Lin, "Liblinear: A library for large linear classification," The Journal of Machine Learning Research, vol. 9, no. 6/1/2008, pp. 1871-1874, 2008.
9. F. Pedregosa, G. Varoquaux, A. Gramfort et al., "Scikit-learn: Machine Learning in Python ," Journal of Machine Learning Research, vol. 12, pp. 2825-2830, 2011.
10. E. Cauvet, N. Hara, S. Dehaene, and C. Pallier, "Investigations of musical and linguistic structures using fMRI." (in prep), 2012.