published: Aug. 26, 2013, recorded: July 2013, views: 8008
Report a problem or upload filesIf 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.
A fundamental limitation of standard machine learning methods is the cost incurred by the preparation of the large training samples required for good generalization. A potential remedy is offered by multi-task learning: in many cases, while individual sample sizes are rather small, there are samples to represent a large number of learning tasks (linear regression problems), which share some constraining or generative property. If this property is suficiently simple it should allow for better learning of the individual tasks despite their small individual sample sizes. In this talk I will review a wide class of multi-task learning methods which encourage low-dimensional representations of the regression vectors. I will describe techniques to solve the underlying optimization problems and present an analysis of the generalization performance of these learning methods which provides a proof of the superiority of multi-task learning under specific conditions.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !