Bundle Methods for Regularized Risk Minimization
A wide variety of machine learning problems can be described as minimizing a regularized risk functional, with different algorithms using different notions of risk and different regularizers. Examples include linear Support Vector Machines (SVMs), Gaussian Processes, Logistic Regression, Conditional Random Fields (CRFs), and Lasso amongst others. This paper describes the theory and implementation of a scalable and modular convex solver which solves all these estimation problems. It can be...[Show more]
|Collections||ANU Research Publications|
|Source:||Journal of Machine Learning Research|
|01_Teo_Bundle_Methods_for_Regularized_2010.pdf||1.79 MB||Adobe PDF||Request a copy|
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