|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 listsAssessment Principles Faculty of Divinity One Day Meeting - Seventh Annual Symposium of the Cambridge Computational Biology Institute
Other talksAdolescent hypermentalizing and the vulnerability to personality disorder Khomeini's perplexed Pakistani men: importing and debating the Iranian Revolution Environmental assessment and temperament analysis of bottlenose dolphins (Tursiops truncatus) kept in different captive conditions 100 years after William Bateson - what can we learn about epistasis by today's statistical machine learning? Systems of forms of the same degree Transformative Industrial Policy for Africa - A study for the United Nations Economic Commission for Africa