Second Order Optimization of Kernel Parameters
published: Dec. 20, 2008, recorded: December 2008, views: 4590
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 investigate the use of second order optimization approaches for solving the multiple kernel learning (MKL) problem. We show that the hessian of the MKL can be computed eﬃciently and this information can be used to compute a better descent direction than the gradient (used in the state-of-the-art SimpleMKL algorithm). We then empirically show that our new approaches outperforms SimpleMKL in terms of computational eﬃciency.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !