The Complexity of Learning Verification
published: Feb. 25, 2007, recorded: October 2004, views: 113
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.
Informally, one branch of learning theory focuses on making statements of the form 'this learned classifier is at least X good'. A common intuition underlying many bounds of this form is that some form of 'prior' or 'bias' must exist on the set of all classifiers in order to make such statements. This intuition can be made precise in a few forms. I'll discuss the ways by which 'bias' and 'prior' allow verifiable learning as well as the limitations of 'prior' in addressing this problem.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !