Recently, many excellent algorithms for time series prediction issues have been proposed,
most of which are developed based on the assumption that sufficient training data and …

We propose and develop SG-ELM, a stable online learning algorithm based on stochastic
gradients and Extreme Learning Machines (ELM). We propose SG-ELM particularly for …

In modern e-healthcare systems, medical institutions can provide more reliable diagnoses
by introducing Machine-Learning (ML)-based classifiers. These ML classifiers are frequently …

A machine learning method needs to adapt to over time changes in the environment. Such
changes are known as concept drift. In this paper, we propose concept drift tackling method …

In the last decade, the online sequential extreme learning machine (OS-ELM) has become
an effective online modeling tool for the regression problem and time series prediction …

The complexity of automotive engines continues to increase to meet increasing performance
requirements such as high fuel economy and low emissions. The increased sensing …

Extreme Learning Machine (ELM) is a promising learning scheme for nonlinear
classification and regression problems and has shown its effectiveness in the machine …

Tracking unknown objects using adaptive tracking-by-detection approaches are widely used
in computer vision. In these approaches, tracking problem is treated as an online …

Robot recognition tasks usually require multiple homogeneous or heterogeneous sensors
which intrinsically generate sequential, redundant, and storage demanding data with …

This paper presents a hybrid algorithm for the creation of heterogeneous single layer neural
networks (SLNNs). The proposed self-adaptive heterogeneous hybrid extreme learning …