There is no doubt that big data are now rapidly expanding in all science and engineering
domains. While the potential of these massive data is undoubtedly significant, fully making …

In big data fields, with increasing computing capability, artificial neural networks have shown
great strength in solving data classification and regression problems. The traditional training …

Countering cyber threats, especially attack detection, is a challenging area of research in the
field of information assurance. Intruders use polymorphic mechanisms to masquerade the …

This paper contributes to the development of randomized methods for neural networks. The
proposed learner model is generated incrementally by stochastic configuration (SC) …

With randomly generated weights between input and hidden layers, a random vector
functional link network is a universal approximator for continuous functions on compact sets …

Neural networks, as powerful tools for data mining and knowledge engineering, can learn
from data to build feature‐based classifiers and nonlinear predictive models. Training neural …

Abstract Random Vector Functional-link (RVFL) networks, a class of learner models, can be
regarded as feed-forward neural networks built with a specific randomized algorithm, ie, the …

With the direct input–output connections, a random vector functional link (RVFL) network is a
simple and effective learning algorithm for single-hidden layer feedforward neural networks …

To address time consuming and parameter sensitivity in the emerging decomposition-
ensemble models, this paper develops a non-iterative learning paradigm without iterative …

This paper presents a fast decorrelated neuro-ensemble with heterogeneous features for
large-scale data analytics, where stochastic configuration networks (SCNs) are employed as …