An extreme learning machine (ELM) is a recently proposed learning algorithm for a single-layer feed forward neural network. In this paper we studied the ensemble of ELM by using a bagging algorithm for facial expression recognition (FER). Facial expression analysis is widely used in the behavior interpretation of emotions, for cognitive science, and social interactions. This paper presents a me...

Providing a satisfactory visual experience is one of the main goals for present-day electronic multimedia devices. All the enabling technologies for storage, transmission, compression, rendering should preserve, and possibly enhance, the quality of the video signal; to do so, quality control mechanisms are required. These mechanisms rely on systems that can assess the visual quality of the inco...

In big data era, the data continuously generated and its distribution may keep changes overtime. These challenges in online stream of data are known as concept drift. In this paper, we proposed the Adaptive Convolutional ELM method (ACNNELM) as enhancement of Convolutional Neural Network (CNN) with a hybrid Extreme Learning Machine (ELM) model plus adaptive capability. This method is aimed for ...

In this paper we explore how precisely the magnetic up/down symmetry must be controlled to insure sharing of edge localized mode (ELM) heat flux between upper and lower diverters in a double-null tokamak. We show for DIH-D, using infrared thermography, that the spatial distribution of Type-I ELM energy is less strongly affected by variations in magnetic geometry than is the time-averaged peak h...

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, ...

Cyber bullying detection that are prevailing commonly in social networks like Twitter is one of the focussed research area. Text mining and detecting cyber bullying has several research challenges and lot of research scope to work with. This research work makes use of supervised feature selection by ranking method in order to choose the features from the tweets. After that extreme learning mach...

The success of myoelectric pattern recognition (M-PR) mostly relies on the features extracted and classifier employed. This paper proposes and evaluates a fast classifier, extreme learning machine (ELM), to classify individual and combined finger movements on amputees and non-amputees. ELM is a single hidden layer feed-forward network (SLFN) that avoids iterative learning by determining input w...

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...

Abstract As a special deep learning algorithm, the multilayer extreme machine (ML-ELM) has been extensively studied to solve practical problems in recent years. The ML-ELM is constructed from autoencoder (ELM-AE), and its generalization performance affected by representation of ELM-AE. However, given label information, unsupervised ELM-AE difficult build discriminative feature space for classif...

Extreme learning machine (ELM) is an efficient learning algorithm for single-hidden layer feedforward networks (SLFN). This paper proposes the combination of ELM networks using a regularized committee. Simulations on many real-world regression data sets have demonstrated that this algorithm generally outperforms the original ELM algorithm.