Pattern classification has been successfully applied in many problem domains, such as
biometric recognition, document classification or medical diagnosis. Missing or unknown …

Most of the traditional pattern classifiers assume their input data to be well-behaved in terms
of similar underlying class distributions, balanced size of classes, the presence of a full set of …

We derive generalization bounds for learning algorithms based on their robustness: the
property that if a testing sample is “similar” to a training sample, then the testing error is close …

We consider regularized support vector machines (SVMs) and show that they are precisely
equivalent to a new robust optimization formulation. We show that this equivalence of robust …

In this paper, we proposed a new robust twin support vector machine (called R-TWSVM) via
second order cone programming formulations for classification, which can deal with data …

In this paper, we propose a primal-dual algorithm with a novel momentum term using the
partial gradients of the coupling function that can be viewed as a generalization of the …

This paper investigates a new learning model in which the input data is corrupted with noise.
We present a general statistical framework to tackle this problem. Based on the statistical …

Motivated by the fact that there may be inaccuracies in features and labels of training data,
we apply robust optimization techniques to study in a principled way the uncertainty in data …

Many machine learning tasks, such as learning with invariance and policy evaluation in
reinforcement learning, can be characterized as problems of learning from conditional …

We propose a novel second order cone programming formulation for designing robust
classifiers which can handle uncertainty in observations. Similar formulations are also …