A scalable modular convex solver for regularized risk minimization

@inproceedings{Teo2007ASM,
  title={A scalable modular convex solver for regularized risk minimization},
  author={Choon Hui Teo and Alexander J. Smola and S. V. N. Vishwanathan and Quoc V. Le},
  booktitle={KDD},
  year={2007}
}
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… CONTINUE READING
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