Extreme learning machine (ELM) has been an important research topic over the last decade due to its high efficiency, easy-implementation, unification of classification and regression, and unification of binary and multi-class learning tasks. Though integrating these advantages, existing ELM algorithms pay little attention to optimizing the choice of kernels, which is indeed crucial to the perfo...

In this paper, a recently developed machine learning algorithm referred to as Extreme Learning Machine (ELM) is used to classify five mental tasks from different subjects using electroencephalogram (EEG) signals available from a well-known database. Performance of ELM is compared in terms of training time and classification accuracy with a Backpropagation Neural Network (BPNN) classifier and al...

Analyzing human gait has earned considerable interest in recent computer vision researches, as it has immense use in deducing the physical well-being of people. Detection of unusual movement patterns can be performed using Support Vector Machines classification with T-Test pre-normalization. Support Vector Machine classifiers are powerful tools, specifically designed to solve large-scale classi...

This paper proposes a new structure of wavelet extreme learning machine i.e. an adaptive wavelet extreme learning machine (AW-ELM) for finger motion recognition using only two EMG channels. The adaptation mechanism is performed by adjusting the wavelet shape based on the input information. The performance of the proposed method is compared to ELM using wavelet (W-ELM0 and sigmoid (Sig-ELM) acti...

Extreme learning machines (ELM), as a learning tool, have gained popularity due to its unique characteristics and performance. However, the generalisation capability of ELM often depends on the nature of the dataset, particularly on whether uncertainty is present in the dataset or not. In order to reduce the effects of uncertainties in ELM prediction and improve its generalisation ability, this...

This paper investigates possibilities for application of Kernel based Extreme Learning Machines (K-ELM) to the problem of multiclass image classification. It is combined with Local Binary Pattern (LBP) image descriptor, to reach highly accurate results. LBP is widely used global image descriptor characterized by compactness and robustness to illumination and resolution changes. Classification i...

In this paper, a wind prediction system for the wind power generation using ensemble of multiple complex extreme learning machines (C-ELM) is proposed. The extreme learning machines is a single layer feed forward neural network having a fast learning and better generalization ability than the gradient-based learning methods. C-ELM is chosen as base classifier because it is very suitable for pro...

Recently, a new learning algorithm for the feedforward neural network named the extreme learning machine (ELM) which can give better performance than traditional tuning-based learning methods for feedforward neural networks in terms of generalization and learning speed has been proposed by Huang et al. In this paper, we first extend the ELM algorithm from the real domain to the complex domain, ...