  In this paper we investigate the usage of regularized correntropy framework
for learning of classifiers from noisy labels. The class label predictors
learned by minimizing transitional loss functions are sensitive to the noisy
and outlying labels of training samples, because the transitional loss
functions are equally applied to all the samples. To solve this problem, we
propose to learn the class label predictors by maximizing the correntropy
between the predicted labels and the true labels of the training samples, under
the regularized Maximum Correntropy Criteria (MCC) framework. Moreover, we
regularize the predictor parameter to control the complexity of the predictor.
The learning problem is formulated by an objective function considering the
parameter regularization and MCC simultaneously. By optimizing the objective
function alternately, we develop a novel predictor learning algorithm. The
experiments on two chal- lenging pattern classification tasks show that it
significantly outperforms the machines with transitional loss functions.
