The massive production and application of nanomaterials (NMs) have raised concerns
about the potential adverse effects of NMs on human health and the environment …

We present a novel diffusion scheme for online kernel-based learning over networks. So far,
a major drawback of any online learning algorithm, operating in a reproducing kernel Hilbert …

We consider multiagent stochastic optimization problems over reproducing kernel Hilbert
spaces. In this setting, a network of interconnected agents aims to learn decision functions …

Adaptive filtering algorithms operating in reproducing kernel Hilbert spaces have
demonstrated superiority over their linear counterpart for nonlinear system identification …

The paper presents a new framework for complex support vector regression (SVR) as well
as Support Vector Machines (SVM) for quaternary classification. The method exploits the …

Despite their attractiveness, popular perception is that techniques for nonparametric function
approximation do not scale to streaming data due to an intractable growth in the amount of …

We consider stochastic nonparametric regression problems in a reproducing kernel Hilbert
space (RKHS), an extension of expected risk minimization to nonlinear function estimation …

Establishing toxicological predictive modeling frameworks for heterogeneous nanomaterials
is crucial for rapid environmental and health risk assessment. However, existing structure …

We present a new framework for online Least Squares algorithms for nonlinear modeling in
RKH spaces (RKHS). Instead of implicitly mapping the data to a RKHS (eg, kernel trick), we …

We propose a novel adaptive learning algorithm based on iterative orthogonal projections in
the Cartesian product of multiple reproducing kernel Hilbert spaces (RKHSs). The objective …