Department of Computer Science | Institute of Theoretical Computer Science | CADMO

Prof. Emo Welzl and Prof. Bernd Gärtner

Mittagsseminar Talk Information |

**Date and Time**: Tuesday, March 22, 2011, 12:15 pm

**Duration**: This information is not available in the database

**Location**: OAT S15/S16/S17

**Speaker**: Kenneth Clarkson (IBM Research Almaden)

I will describe randomized approximation algorithms for some classical problems of machine learning, where the algorithms have provable bounds that hold with high probability. Some of our algorithms are sublinear, that is, they do not need to touch all the data. Specifically, for a set of points a_1...a_n in d dimensions, we show that finding a d-vector x that approximately maximizes the margin min_i a_i dot x can be done in O(n+d)/epsilon^2 time, up to logarithmic factors, where epsilon>0 is an additive approximation parameter. Our algorithm is a primal-dual version of the classical perceptron training algorithm, in which both the primal and the dual variables are updated using randomization. We have a similar result for the problem of finding the smallest ball containing the input points. We also give versions of our algorithms for the Gaussian and polynomial kernels, with an additional runtime cost of O(1/epsilon^5).

Joint work with Elad Hazan and David Woodruff.

Upcoming talks | All previous talks | Talks by speaker | Upcoming talks in iCal format (beta version!)

Previous talks by year: 2023 2022 2021 2020 2019 2018 2017 2016 2015 2014 2013 2012 2011 2010 2009 2008 2007 2006 2005 2004 2003 2002 2001 2000 1999 1998 1997 1996

Information for students and suggested topics for student talks

Automatic MiSe System Software Version 1.4803M | admin login