Democratic Approximation of Lexicographic Preference Models
published: Aug. 29, 2008, recorded: July 2008, views: 112
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.
Previous algorithms for learning lexicographic preference models (LPMs) produce a "best guess" LPM that is consistent with the observations. Our approach is more democratic: we do not commit to a single LPM. Instead, we approximate the target using the votes of a collection of consistent LPMs. We present two variations of this method -- "variable voting" and "model voting" -- and empirically show that these democratic algorithms outperform the existing methods. We also introduce an intuitive yet powerful learning bias to prune some of the possible LPMs. We demonstrate how this learning bias can be used with variable and model voting and show that the learning bias improves the learning curve significantly, especially when the number of observations is small.
Download slides: icml08_walsh_dalpm_01.pdf (771.0 KB)
Download slides: icml08_walsh_dalpm_01.ppt (1.1 MB)
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !