Learning from Incomplete Data with Infinite Imputations
published: July 28, 2008, recorded: July 2008, views: 3962
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.
We address the problem of learning decision functions from training data in which some attribute values are unobserved. This problem can arise for instance, when training data is aggregated from multiple sources, and some sources record only a subset of attributes. We derive a joint optimization problem for the final classifier in which the distribution governing the missing values is a free parameter. We show that the optimal solution concentrates the density mass on finitely many atoms, and provide a corresponding algorithm for learning from incomplete data. We report on empirical results on benchmark data, and on the email spam application that motivates the problem setting
Download slides: icml08_dick_lfid_01.pdf (1.1 MB)
Download slides: icml08_dick_lfid_01.ppt (2.2 MB)
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !