729 citations · 871 across the 7 of their papers we have counts for
7 papers
Two Dimensional Stochastic Configuration Networks for Image Data Analytics
Ming Li, Dianhui Wang
Stochastic configuration networks (SCNs) as a class of randomized learner model have been successfully employed in data analytics due to its universal approximation capability and…
Deep Stacked Stochastic Configuration Networks for Lifelong Learning of Non-Stationary Data Streams
Mahardhika Pratama, Dianhui Wang
The concept of SCN offers a fast framework with universal approximation guarantee for lifelong learning of non-stationary data streams. Its adaptive scope selection property enable…
Randomized Mixture Models for Probability Density Approximation and Estimation
Hien D. Nguyen, Dianhui Wang, Geoffrey J. McLachlan
Randomized neural networks (NNs) are an interesting alternative to conventional NNs that are more used for data modeling. The random vector functional-link (RVFL) network is an est…
Stochastic Configuration Networks Ensemble for Large-Scale Data Analytics
Dianhui Wang, Caihao Cui
This paper presents a fast decorrelated neuro-ensemble with heterogeneous features for large-scale data analytics, where stochastic configuration networks (SCNs) are employed as ba…
Stochastic Configuration Networks: Fundamentals and Algorithms
Dianhui Wang, Ming Li
This paper contributes to a development of randomized methods for neural networks. The proposed learner model is generated incrementally by stochastic configuration (SC) algorithms…
Deep Stochastic Configuration Networks with Universal Approximation Property
Dianhui Wang, Ming Li
This paper develops a randomized approach for incrementally building deep neural networks, where a supervisory mechanism is proposed to constrain the random assignment of the weigh…
Robust Stochastic Configuration Networks with Kernel Density Estimation
Dianhui Wang, Ming Li
Neural networks have been widely used as predictive models to fit data distribution, and they could be implemented through learning a collection of samples. In many applications, h…