The so-called pinball loss for estimating conditional quantiles is a well-known tool in both
statistics and machine learning. So far, however, only little work has been done to quantify …

In this paper, we study an online learning algorithm with a robust loss function L σ for
regression over a reproducing kernel Hilbert space (RKHS). The loss function L σ involving …

In microeconomics, a topic of interest is the estimation of production functions. By definition,
a production function is a non-decreasing function that envelops all the observations (firms) …

Within the statistical learning framework, this paper studies the regression model associated
with the correntropy induced losses. The correntropy, as a similarity measure, has been …

Pairwise learning naturally arises from machine learning tasks such as AUC maximization,
ranking, and metric learning. In this paper we propose a new pairwise learning algorithm …

Cross-validation (CV) is a widely adopted approach for selecting the optimal model.
However, the computation of empirical cross-validation error (CVE) has high complexity due …

This letter addresses the robustness problem when learning a large margin classifier in the
presence of label noise. In our study, we achieve this purpose by proposing robustified large …

Abstract Model selection is one of the key issues both in recent research and application of
kernel methods. Cross-validation is a commonly employed and widely accepted model …

Support vector machines (SVMs) are special kernel based methods and have been among
the most successful learning methods for more than a decade. SVMs can informally be …

Support vector machines (SVMs) have attracted much attention in theoretical and in applied
statistics. The main topics of recent interest are consistency, learning rates and robustness …