Prepare for the Worst, Hope for the Best: Active Robust Learning On Distributions.

In recent years, many learning systems have been developed for higher level forms of data, such as learning on distributions in which each example itself is a distribution. This article proposes active robust learning on distributions. In learning on distributions, there is no access to distributions themselves but rather access is through a sample drawn from a distribution.

Convergence and performance analysis of kernel regularized robust recursive least squares.

Kernel recursive least squares (KRLS) is very sensitive to non-Gaussian noise and hence, robust extensions are proposed using maximum correntropy criterion or generalized maximum correntropy. However, because of the complex form of the model, there is no theoretical analysis on the convergence of these filters. In this paper, we propose a new alternative: Kernel Regularized Robust RLS (KRLS).

Universal Approximation by Using the Correntropy Objective Function.

Several objective functions have been proposed in the literature to adjust the input parameters of a node in constructive networks. Furthermore, many researchers have focused on the universal approximation capability of the network based on the existing objective functions. In this brief, we use a correntropy measure based on the sigmoid kernel in the objective function to adjust the input parameters of a newly added node in a cascade network.

Temporal and spatial monitoring and prediction of epidemic outbreaks.

This paper introduces a nonlinear dynamic model to study spatial and temporal dynamics of epidemics of susceptible-infected-removed type. It involves modeling the respective collections of epidemic states and syndromic observations as random finite sets. Each epidemic state consists of the number of infected individuals in an isolated population system and the corresponding partially known parameters of the epidemic model.

Robust support vector machine-trained fuzzy system.

Because the SVM (support vector machine) classifies data with the widest symmetric margin to decrease the probability of the test error, modern fuzzy systems use SVM to tune the parameters of fuzzy if-then rules. But, solving the SVM model is time-consuming. To overcome this disadvantage, we propose a rapid method to solve the robust SVM model and use it to tune the parameters of fuzzy if-then rules.