Extreme learning machine (ELM) represents one of the recent successful approaches in machine learning, particularly for performing pattern classification. One key strength of ELM is the significantly low computational time required for training new classifiers since the weights of the hidden and output nodes are randomly chosen and analytically determined, respectively. In this paper, we addres...

In this paper, we propose an image classification method for improving the learning speed of convolutional neural networks (CNN). Although CNN is widely used in multiclass image classification datasets, the learning speed remains slow for large amounts of data. Therefore, we attempted to improve the learning speed by applying an extreme learning machine (ELM). We propose a learning method combi...

Landslide susceptibility prediction has the disadvantages of being challenging to apply expanding landslide samples and low accuracy a subjective random selection non-landslide samples. Taking Fu’an City, Fujian Province, as an example, model based on semi-supervised framework using particle swarm optimization optimize extreme learning machines (SS-PSO-ELM) is proposed. Based samples, construct...

This comment letter points out that the essence of the "extreme learning machine (ELM)" recently appeared has been proposed earlier by Broomhead and Lowe and Pao , and discussed by other authors. Hence, it is not necessary to introduce a new name "ELM."

This comment letter points out that the essence of the “extreme learning machine (ELM)” recently appeared has been proposed earlier by Broomhead and Lowe and Pao et al., and discussed by other authors. Hence, it is not necessary to introduce a new name “ELM.”

As real industrial processes have measurement samples with noises of different statistical characteristics and obtain the sample one by one usually, on-line sequential learning algorithms which can achieve better learning performance for systems with noises of various statistics are necessary. This paper proposes a new online Extreme Learning Machine (ELM, of Huang et al.) algorithm, namely rec...

Extreme learning machine (ELM) [G.-B. Huang, Q.-Y. Zhu, C.-K. Siew, Extreme learning machine: a new learning scheme of feedforward neural networks, in: Proceedings of the International Joint Conference on Neural Networks (IJCNN2004), Budapest, Hungary, 25–29 July 2004], a novel learning algorithm much faster than the traditional gradient-based learning algorithms, was proposed recently for sing...

In this paper, we propose a novel deep learning method for feature extraction in prognostics and health management applications. The proposed method is based on Extreme Learning Machines (ELM) and Auto-Encoders (AE), which have demonstrated very good performance and very short training time compared to other deep learning methods on several applications, including image recognition problems. Th...

Regression is one of the most basic problems in data mining. For regression problem, extreme learning machine (ELM) can get better generalization performance at a much faster learning speed. However, the enlarging volume of datasets makes regression by ELM on very large scale datasets a challenging task. Through analyzing the mechanism of ELM algorithm, an efficient parallel ELM for regression ...

In this paper, a novel approach proposed for structural damage detection from limited number of sensors using extreme learning machine (ELM). As the number of sensors used to measure modal data is normally limited and usually are less than the number of DOFs in the finite element model, the model reduction approach should be used to match with incomplete measured mode shapes. The second-order a...