Friday, November 2, 2012
11:45 am - 1:00 pm
Hudson Hall 208
Clayton Scott, Ph.D., Associate Professor, Electrical Engineering and Computer Science, University of Michigan
In many real-world classification problems, the labels of training examples are randomly corrupted. That is, the set of training examples for each class is contaminated by examples of the other class. Existing approaches to this problem assume that the two classes are separable, that the label noise is independent of the true class label, or that the noise proportions for each class are known. I introduce a general framework for classification with label noise that eliminates these assumptions.