In this article, we improve extreme learning machines for regression tasks using a graph signal processing based regularization. We assume that the target signal for prediction or regression is a graph signal. With this assumption, we use the regularization to enforce that the output of an extreme learning machine is smooth over a given graph. Simulation results with real data confirm that such...

Smile detection is a specialized task in facial expression analysis with applications such as photo selection, user experience analysis, and patient monitoring. As one of the most important and informative expressions, smile conveys the underlying emotion status such as joy, happiness, and satisfaction. In this paper, an efficient smile detection approach is proposed based on Extreme Learning M...

It is clear that the learning speed of feedforward neural networks is in general far slower than required and it has been a major bottleneck in their applications for past decades. Two key reasons behind may be: (1) the slow gradient-based learning algorithms are extensively used to train neural networks, and (2) all the parameters of the networks are tuned iteratively by using such learning al...

In this paper, we describe an approximate method for reducing the time and memory complexities of the kernel Extreme Learning Machine variants. We show that, by adopting a Nyström-based kernel ELM matrix approximation, we can define an ELM space exploiting properties of the kernel ELM space that can be subsequently used to apply several optimization schemes proposed in the literature for ELM ne...

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."

Xia Sun 1,*, Jingting Xu 1, Changmeng Jiang 1, Jun Feng 1, Su-Shing Chen 2 and Feijuan He 3 1 School of Information Science and Technology, Northwest University, Xi’an 710069, China; [email protected] (J.X.); [email protected] (C.J.); [email protected] (J.F.) 2 Computer Information Science and Engineering, University of Florida, Gainesville, FL 32608, USA; [email protected] 3 Department o...

This paper highlights the prediction of learning disabilities (LD) in school-age children using rough set theory (RST) with an emphasis on application of data mining. In rough sets, data analysis start from a data table called an information system, which contains data about objects of interest, characterized in terms of attributes. These attributes consist of the properties of learning disabil...

This paper proposes a novel method for load forecast, which integrates wavelet transform and extreme learning machine. In order to capture more internal features, wavelet transform is used to decompose the load series into a set of subcomponents, which are more predictable. Then all the components are separately processed by extreme learning machine. Numerical testing shows that the proposed me...

This paper presents a short introduction to the Reservoir Computing and Extreme Learning Machine main ideas and developments. While both methods make use of Neural Networks and Random Projections, Reservoir Computing allows the network to have a recurrent structure, while the Extreme Learning Machine is a Feedforward neural network only. Some state of the art techniques are briefly presented an...