Support Vector Machines (SVMs) algorithms and techniques have been extensively used over the last fifteen years in a variety of machine learning applications. This talk presents a brief tutorial to standard SVMs and discusses recent advances in learning kernels where the user is not anymore required to commit to a particular kernel, but only to specify a family of kernels. The learning algorithm then uses the data to both select the kernel out of this family and determine the prediction hypothesis.
The talk includes joint work with Mehryar Mohri and Afshin Rostamizadeh.
Corinna Cortes is the Head of Google Research, NY, where she is working on a broad range of theoretical and applied large-scale machine learning problems. Prior to Google, Corinna spent more than ten years at AT&T Labs - Research, formerly AT&T Bell Labs, where she held a distinguished research position. Corinna's research work is well-known in particular for her contributions to the theoretical foundations of support vector machines (SVMs) for which she jointly with Vladimir Vapnik received the 2008 Paris Kanellakis Theory and Practice Award, and for her work on data-mining in very large data sets for which she was awarded the AT&T Science and Technology Medal in the year 2000. Corinna received her MS degree in Physics from the Niels Bohr Institute in Copenhagen and joined AT&T Bell Labs as a researcher in 1989. She received her Ph.D. in computer science from the University of Rochester in 1993. Corinna is also a competitive runner, placing third in the More Marathon in New York City in 2005, and a mother of two.
Refreshments provided at 10:45