Training with noise is equivalent to Tikhonov regularization
Neural Computation
Machine Learning
Robust Solutions to Least-Squares Problems with Uncertain Data
SIAM Journal on Matrix Analysis and Applications
Parameter Estimation in the Presence of Bounded Data Uncertainties
SIAM Journal on Matrix Analysis and Applications
Mathematics of Operations Research
Prediction with Gaussian processes: from linear regression to linear prediction and beyond
Learning in graphical models
Lectures on modern convex optimization: analysis, algorithms, and engineering applications
Lectures on modern convex optimization: analysis, algorithms, and engineering applications
A robust minimax approach to classification
The Journal of Machine Learning Research
Convex Optimization
On Constraint Sampling in the Linear Programming Approach to Approximate Dynamic Programming
Mathematics of Operations Research
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
The Interplay of Optimization and Machine Learning Research
The Journal of Machine Learning Research
Learning from incomplete data with infinite imputations
Proceedings of the 25th international conference on Machine learning
Boosting with incomplete information
Proceedings of the 25th international conference on Machine learning
Max-margin Classification of Data with Absent Features
The Journal of Machine Learning Research
Privacy-Preserving Data Publishing
Foundations and Trends in Databases
Robustness and Regularization of Support Vector Machines
The Journal of Machine Learning Research
Maximum Relative Margin and Data-Dependent Regularization
The Journal of Machine Learning Research
Exploiting uncertain data in support vector classification
KES'07/WIRN'07 Proceedings of the 11th international conference, KES 2007 and XVII Italian workshop on neural networks conference on Knowledge-based intelligent information and engineering systems: Part III
IEEE Transactions on Information Theory
Learning algorithms for link prediction based on chance constraints
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part I
Classification with Incomplete Data Using Dirichlet Process Priors
The Journal of Machine Learning Research
Theory and Applications of Robust Optimization
SIAM Review
A large-update primal-dual interior-point method for second-order cone programming
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part I
A Distributional Interpretation of Robust Optimization
Mathematics of Operations Research
Semiconducting bilinear deep learning for incomplete image recognition
Proceedings of the 2nd ACM International Conference on Multimedia Retrieval
Optimization Under Probabilistic Envelope Constraints
Operations Research
Robust twin support vector machine for pattern classification
Pattern Recognition
Review: Supervised classification and mathematical optimization
Computers and Operations Research
Expert Systems with Applications: An International Journal
Efficient methods for robust classification under uncertainty in kernel matrices
The Journal of Machine Learning Research
A second order cone programming approach for semi-supervised learning
Pattern Recognition
Robust feature selection for SVMs under uncertain data
ICDM'13 Proceedings of the 13th international conference on Advances in Data Mining: applications and theoretical aspects
Alternative second-order cone programming formulations for support vector classification
Information Sciences: an International Journal
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We propose a novel second order cone programming formulation for designing robust classifiers which can handle uncertainty in observations. Similar formulations are also derived for designing regression functions which are robust to uncertainties in the regression setting. The proposed formulations are independent of the underlying distribution, requiring only the existence of second order moments. These formulations are then specialized to the case of missing values in observations for both classification and regression problems. Experiments show that the proposed formulations outperform imputation.