published: Feb. 25, 2007, recorded: February 2006, views: 562
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.
The Biological domain poses new challenges for statistical learning. In the talk we shall analyze and theoretically explain some counter-intuitive experimental and theoretical findings that systematic reversal of classifier decisions can occur when switching from training to independent test data (the phenomenon of anti-learning). We demonstrate this on both natural and synthetic data and show that it is distinct from overfitting. The natural datasets discussed will include: prediction of response to chemo-radio-therapy for esophageal cancer from gene expression (measured by cDNA-microarrays); prediction of genes affecting the aryl hydrocarbon receptor pathway in yeast. The main synthetic classification problem will be the approximation of samples drawn from high dimensional distributions, for which a theoretical explanation will be outlined.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !