Pointwise Tracking the Optimal Regression Function

author: Yair Wiener, Computer Science Department, Technion - Israel Institute of Technology
published: Jan. 14, 2013,   recorded: December 2012,   views: 2926


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.


This paper examines the possibility of a "reject option" in the context of least squares regression. It is shown that using rejection it is theoretically possible to learn "selective" regressors that can ϵ-pointwise track the best regressor in hindsight from the same hypothesis class, while rejecting only a bounded portion of the domain. Moreover, the rejected volume vanishes with the training set size, under certain conditions. We then develop efficient and exact implementation of these selective regressors for the case of linear regression. Empirical evaluation over a suite of real-world datasets corroborates the theoretical analysis and indicates that our selective regressors can provide substantial advantage by reducing estimation error.

See Also:

Download slides icon Download slides: machine_wiener_pointwise_tracking_01.pdf (624.0 KB)

Download article icon Download article: machine_wiener_pointwise_tracking_01.pdf (151.8 KB)

Help icon Streaming Video Help

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: