On Surrogate Loss Functions, f-Divergences and Decentralized Detection
published: July 30, 2009, recorded: June 2009, views: 916
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.
In 1951, David Blackwell published a seminal paper - widely cited in economics - in which a link was established between the risk based on 0-1 loss and a class of functionals known as f-divergences. The latter functionals have since come to play an important role in several areas of signal processing and information theory, including decentralized detection. Yet their role in these fields has largely been heuristic. We show that an extension of Blackwell´s programme provides a solid foundation for the use of f-divergences in decentralized detection, as well as in more general problems of experimental design. Our extension is based on a connection between f-divergences and the class of so-called surrogate loss funcions - computationally-inspired upper bounds on 0-1 loss that have become central in the machine learning literature on classification. (Joint work with XuanLong Nguyen and Martin Wainwright.)
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !