A great many computational algorithms developed over the past half-century have been
motivated or suggested by biological systems or processes, the most well-known being the …

In the last few years, the support vector machine (SVM) method has motivated new interest
in kernel regression techniques. Although the SVM has been shown to exhibit excellent …

Gaussian process models constitute a class of probabilistic statistical models in which a
Gaussian process (GP) is used to describe the Bayesian a priori uncertainty about a latent …

Nonlinear dynamics are ubiquitous in science and engineering applications, but the physics
of most complex systems is far from being fully understood. Discovering interpretable …

We demonstrate how a variational approximation scheme enables effective inference of key
parameters in probabilisitic signal models which employ the Student-t distribution. Using the …

A robust and adaptive multivariate nonlinear time series prediction model is proposed based
on echo state network and variational inference and we call it robust variational echo state …

This study concerns with the control of basic oxygen furnace (BOF) steelmaking process and
proposes a dynamic control model based on adaptive-network-based fuzzy inference …

In order to achieve reliable autonomous control in advanced robotic systems like
entertainment robots, assistive robots, humanoid robots and autonomous vehicles, sensory …

This paper presents a new learning formulation for multiple model estimation (MME). Under
this formulation, training data samples are generated by several (unknown) statistical …

Gaussian Processes (GP) have become popular machine learning methods for kernel
based learning on datasets with complicated covariance structures. In this paper, we present …