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Found 8 papers, 0 papers with code
Stochastic Configuration Machines: FPGA Implementation
Stochastic Configuration Machines (SCMs) extend this to focus on reducing the memory constraints by limiting the randomized weights to a binary value with a scalar for each node and using a mechanism model to improve the learning performance and result interpretability.
Stochastic Configuration Machines for Industrial Artificial Intelligence
Real-time predictive modelling with desired accuracy is highly expected in industrial artificial intelligence (IAI), where neural networks play a key role.
Two Dimensional Stochastic Configuration Networks for Image Data Analytics
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 fast modelling property.
Deep Stacked Stochastic Configuration Networks for Lifelong Learning of Non-Stationary Data Streams
The concept of SCN offers a fast framework with universal approximation guarantee for lifelong learning of non-stationary data streams.
Stochastic Configuration Networks Ensemble for Large-Scale Data Analytics
Based on the group of heterogeneous features, the block Jacobi and Gauss-Seidel methods are employed to iteratively evaluate the output weights, and a convergence analysis is given with a demonstration on the uniqueness of these iterative solutions.
Deep Stochastic Configuration Networks with Universal Approximation Property
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 weights and biases, and all the hidden layers have direct links to the output layer.
Robust Stochastic Configuration Networks with Kernel Density Estimation
The kernel density estimation (KDE) method is employed to set the penalty weights for each training samples, so that some negative impacts, caused by noisy data or outliers, on the resulting learner model can be reduced.
Stochastic Configuration Networks: Fundamentals and Algorithms
This paper contributes to a development of randomized methods for neural networks.
