Bundle methods and its application in machine learning
Add to your list(s)
Download to your calendar using vCal
If you have a question about this talk, please contact Shakir Mohamed.
We will present work on bundle methods for machine learning. In this talk, we would like to first briefly review some basic concepts in convex optimization, using quadratic programming as an example. After that, we will give some examples of convex objective functions that are widely used in machine learning. Then we will talk about the cuttingplane method and bundle methods, together with the convergence analysis. We may also talk a little bit on how these methods can be extended to the optimization of nonconvex functions.
The following two papers can be used as our references:
TrinhMinhTri Do and Thierry Arti`eres, “Large Margin Training for Hidden Markov Models with Partially Observed States”, in Proc.
ICML 2009.
Alexander, J.S. ,Vishwanathan, S.V.N. and Quoc V.L. “Bundle methods for machine learning”, in Proc.NIPS2007
This talk is part of the Machine Learning Reading Group @ CUED series.
This talk is included in these lists:
Note that exdirectory lists are not shown.
