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2007
ACM

A scalable modular convex solver for regularized risk minimization

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A scalable modular convex solver 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), Logistic Regression, Conditional Random Fields (CRFs), and Lasso amongst others. This paper describes the theory and implementation of a highly scalable and modular convex solver which solves all these estimation problems. It can be parallelized on a cluster of workstations, allows for data-locality, and can deal with regularizers such as 1 and 2 penalties. At present, our solver implements 20 different estimation problems, can be easily extended, scales to millions of observations, and is up to 10 times faster than specialized solvers for many applications. The open source code is freely available as part of the ELEFANT toolbox. Categories and Subject Descriptors I.2.6 [Artificial Intelligence]: Learning General Terms Algorithms, Optimizati...
Choon Hui Teo, Alex J. Smola, S. V. N. Vishwanatha
Added 30 Nov 2009
Updated 30 Nov 2009
Type Conference
Year 2007
Where KDD
Authors Choon Hui Teo, Alex J. Smola, S. V. N. Vishwanathan, Quoc V. Le
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