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Abstract

Reference (APA Format)

Schmah T., Hinton G., Zemel R., Small S.L. & Strother S.C. 2008. Generative versus discriminate RBM models for classification of fMRI images. NIPS 2008, , .

Title

Generative versus discriminate RBM models for classification of fMRI images

Journal Name

NIPS 2008

Abstract

Neuroimaging datasets often have a very large number of voxels and a very small number of training cases, which means that overfitting of models for this data can become a very serious problem. Working with a set of fMRI images from a study on stroke recovery, we consider a classification task for which logistic regression performs poorly, even when L1- or L2- regularized. We show that much better discrimination can be achieved by fitting a generative model to each separate condition and then seeing which model is most likely to have generated the data. We compare discriminative training of exactly the same set of models, and we also consider convex blends of generative and discriminative training.

Year

2008

Pages

Authors

Schmah T., Hinton G., Zemel R., Small S.L. & Strother S.C.

Conference Title

Twenty-Second Annual Conference on Neural Information Processing Systems

Conference Host

NIPS

Presentation Type

Poster

Location

Vancouver, BC, Canada (December 8-10, 2008)

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