Ensemble approaches introduced in the Extreme Learning Machine literature mainly come from methods that rely on data sampling procedures, under assumption training are heterogeneously enough to set up diverse base learners. To overcome this assumption, it was proposed an ELM ensemble method based Negative Correlation framework, called (NCELM). This model works two stages: (i) different ELMs gen...

The detection of stress is important because it contributes to diverse pathophysiological changes including sudden death. Various techniques have been used evaluate in terms questionnaire or by quantifying the physiological signals. Electroencephalogram signals are highly useful measuring human stress. Therefore, solve and detect problem, this work had extracted electroencephalogram features th...

Electric load forecasting plays an important role in electricity markets and power systems. Because electric load time series are complicated and nonlinear, it is very difficult to achieve a satisfactory forecasting accuracy. In this paper, a hybrid model, Wavelet Denoising-Extreme Learning Machine optimized by k-Nearest Neighbor Regression (EWKM), which combines k-Nearest Neighbor (KNN) and Ex...

A common feature in many hard pattern recognition problems is the fact that the object of interest is statistically overwhelmed by others. The overall aim of the “Learning, Evolution and Extreme Statistics” (AE3 being its Spanish acronym) project is to study those problems in the following concrete areas: 1. Natural image statistics and applications. 2. New classification techniques in extreme ...

In this paper, a novel learning algorithm termed Hybrid Online Sequential Extreme Learning Machine (HOSELM) is proposed. The proposed HOS-ELM algorithm is a fusion of the Online Sequential Extreme Learning Machine (OS-ELM) and the Minimal Resource Allocation Network (MRAN). It is capable of reducing the number of hidden nodes in Single-hidden Layer Feed-forward Neural Networks (SLFNs) with Radi...

Extreme learning machine is a new scheme for learning the feedforward neural network, where the input weights and biases determining the nonlinear feature mapping are initiated randomly and are not learned. In this work we analyse approximation ability of the extreme learning machine depending on the activation function type and ranges from which input weights and biases are randomly generated....