|COOKIES: By using this website you agree that we can place Google Analytics Cookies on your device for performance monitoring.|
Bundle methods and its application in machine learning
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 cutting-plane 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 non-convex functions.
The following two papers can be used as our references:
Trinh-Minh-Tri 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.NIPS-2007
This talk is part of the Machine Learning Reading Group @ CUED series.
This talk is included in these lists:
Note that ex-directory lists are not shown.
Other listsCRUK-CI Genomics Core seminar series PDN Postdoc Symposium Plenary talks Applied and Computational Analysis
Other talksMaking NanoMedicine Personal: Translating Genome-Wide Information & Point of Care Diagnostics into the Clinic Lunchtime Talk: Helen's Bedroom Comparative aspects of canine cancer and therapy Filtration Flow in Poro-Visco-Elastic Media Mitochondrial optic neuropathies and beyond: the common theme of mitochondrial dynamics and mechanistic insights Tracing memory circuits in Drosophila using whole-brain electron microscopy