Feedforward neural networks (FFNN) are among the most used neural networks for
modeling of various nonlinear problems in engineering. In sequential and especially real …

An obvious defect of extreme learning machine (ELM) is that its prediction performance is
sensitive to the random initialization of input-layer weights and hidden-layer biases. To …

The learning algorithm of an extreme learning machine (ELM) has two fundamental steps: 1)
random nonlinear feature transformation and 2) least squares learning. Since the …

One obvious defect of Extreme Learning Machine (ELM) is that the prediction performance
of ELM is sensitive to the random initialization of input-layer weights and hidden-layer …

One obvious defect of Extreme Learning Machine (ELM) is that the prediction performance
of ELM is sensitive to the random initialization of input-layer weights and hidden-layer …

This paper proposes a new method to train stable extreme learning machines (ELM). The
new method, called StaELM, uses correlation coefficients in Gaussian process to measure …

It is an effective way to overcome the randomization sensibility of extreme learning machine
(ELM) by using Gaussian process regression (GPR) to optimize the output-layer weights …

O aprendizado de máquina requer a indução de modelos preditivos. Frequentemente, há
dois problemas relacionados com essa tarefa: o custo de rotulação e o tempo de …

Gaussian processes (GPs) constitute one of the most important Bayesian machine learning
approaches. Several researchers have considered postulating mixtures of Gaussian …