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CORR
2008
Springer
113views Education» more  CORR 2008»
13 years 4 months ago
Robustness, Risk, and Regularization in Support Vector Machines
We consider two new formulations for classification problems in the spirit of support vector machines based on robust optimization. Our formulations are designed to build in prote...
Huan Xu, Shie Mannor, Constantine Caramanis
CDC
2009
IEEE
117views Control Systems» more  CDC 2009»
13 years 9 months ago
Risk sensitive robust support vector machines
— We propose a new family of classification algorithms in the spirit of support vector machines, that builds in non-conservative protection to noise and controls overfitting. O...
Huan Xu, Constantine Caramanis, Shie Mannor, Sungh...
AAAI
2006
13 years 5 months ago
Robust Support Vector Machine Training via Convex Outlier Ablation
One of the well known risks of large margin training methods, such as boosting and support vector machines (SVMs), is their sensitivity to outliers. These risks are normally mitig...
Linli Xu, Koby Crammer, Dale Schuurmans
NIPS
2007
13 years 5 months ago
Bundle Methods for Machine Learning
We present a globally convergent method for regularized risk minimization problems. Our method applies to Support Vector estimation, regression, Gaussian Processes, and any other ...
Alex J. Smola, S. V. N. Vishwanathan, Quoc V. Le
KDD
2007
ACM
132views Data Mining» more  KDD 2007»
14 years 4 months ago
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 r...
Choon Hui Teo, Alex J. Smola, S. V. N. Vishwanatha...