Feature Induction Using Boosting and Logistic Regression on fMRI Images

author: Melissa K. Carroll, Department of Computer Science, Princeton University
published: Feb. 25, 2007,   recorded: December 2006,   views: 8695

Related Open Educational Resources

Related content

Report a problem or upload files

If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Lecture popularity: You need to login to cast your vote.


Early efforts in fMRI classification were limited in that individual voxels were used as features (e.g. [1]), yet voxels divide images into regions that do not directly correspond to underlying neural activity. A growing trend is to perform spatial smoothing that captures the correlation between nearby voxels. Unfortunately, the optimal spatial resolution for this smoothing is unknown and likely varies across brain regions and cognitive tasks. The present work describes two methods that induce features of varying size and shape and use them to produce additive models that offer the potential for easy interpretability.

Link this page

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

Write your own review or comment:

make sure you have javascript enabled or clear this field: