Crowd density estimation in public areas with people gathering and waiting is the important content of intelligent crowd surveillance. A real-time and high accuracy algorithm is necessary to be inputted in the classification and regression of crowd density estimation to improve the speed and increase the efficiency. Extreme Learning Machine (ELM) is a neural network architecture in which hidden...

The automatic sound event classification (SEC) has attracted a growing attention in recent years. Feature extraction is a critical factor in SEC system, and the deep neural network (DNN) algorithms have achieved the state-of-the-art performance for SEC. The extreme learning machine-based auto-encoder (ELM-AE) is a new deep learning algorithm, which has both an excellent representation performan...

To address the imbalanced classification problem emerging in Bioinformatics, a boundary movement-based extreme learning machine (ELM) algorithm called BM-ELM was proposed. BM-ELM tries to firstly explore the prior information about data distribution by condensing all training instances into the one-dimensional feature space corresponding to the original output in ELM, and then on the transforme...

This paper presented two maintainability prediction models that are developed and compared for object-oriented software systems based on the recently introduced learning algorithm called Sensitivity Based Linear Learning Method (SBLLM) and extreme learning machines (ELM). As the number of object-oriented software systems increases, it becomes more important for organizations to maintain those s...

Our paper emphasizes the relevance of Extreme Learning Machine (ELM) in Bioinformatics applications by addressing the problem of predicting the disulfide connectivity from protein sequences. We test different activation functions of the hidden neurons and we show that for the task at hand the Radial Basis Functions are the best performing. We also show that the ELM approach performs better than...

A machine learning method needs to adapt to over time changes in the environment. Such changes are known as concept drift. In this paper, we propose concept drift tackling method as an enhancement of Online Sequential Extreme Learning Machine (OS-ELM) and Constructive Enhancement OS-ELM (CEOS-ELM) by adding adaptive capability for classification and regression problem. The scheme is named as ad...

Extreme Learning Machines (ELMs) are a versatile Machine (ML) algorithm that features as the main advantage possibility of seamless implementation with physical systems. Yet, despite success implementations ELMs, there is still lack fundamental understanding in regard to their optical implementations. In this context, work makes use an complex media and wavefront shaping techniques implement EL...

The paper presents an approach for performing regression on large data sets in reasonable time, using an ensemble of extreme learning machines (ELMs). The main purpose and contribution of this paper are to explore how the evaluation of this ensemble of ELMs can be accelerated in three distinct ways: (1) training and model structure selection of the individual ELMs are accelerated by performing ...

Brain–computer interface (BCI) systems based on electroencephalography have been increasingly used in different contexts, engendering applications from entertainment to rehabilitation in a non-invasive framework. In this study, we perform a comparative analysis of different signal processing techniques for each BCI system stage concerning steady state visually evoked potentials (SSVEP), which i...

Extreme Support Vector Machine (ESVM), a variant of ELM, is a nonlinear SVM algorithm based on regularized least squares optimization. In this chapter, a regression algorithm, Extreme Support Vector Regression (ESVR), is proposed based on ESVM. Experiments show that, ESVR has a better generalization ability than the traditional ELM.Furthermore, ESVMcan reach comparable accuracy as SVR and LS-SV...