Abstract Human gait analysis is a novel topic in the field of computer vision with many famous applications like prediction osteoarthritis and patient surveillance. In this application, abnormal behavior problems walking style detected suspected patients. The means assessments terms knee joints any other symptoms that directly affected patients’ style. carries substantial importance medical dom...

Extreme multi-label learning (XML) or classification has been a practical and important problem since the boom of big data. The main challenge lies in the exponential label space which involves 2 possible label sets when the label dimension L is very large, e.g., in millions for Wikipedia labels. This paper is motivated to better explore the label space by building and modeling an explicit labe...

Abstract Today, intelligent systems that offer artificial intelligence capabilities often rely on machine learning. Machine learning describes the capacity of to learn from problem-specific training data automate process analytical model building and solve associated tasks. Deep is a concept based neural networks. For many applications, deep models outperform shallow traditional analysis approa...

Deep learning has been proven to outperform many conventional machine learning algorithms (e. g., Support Vector Machines) in many fields such as image processing and text analyses. This is due to its outstanding capability to model complex data distributions. However, as networks become deeper, there is an increased risk of overfitting and higher sensitivity to noise. Stacked Denoising Autoenc...

Feedforward neural networks (FFNN) have been utilised for various research in machine learning and they have gained a significantly wide acceptance. However, it was recently noted that the feedforward neural network has been functioning slower than needed. As a result, it has created critical bottlenecks among its applications. Extreme Learning Machines (ELM) were suggested as alternative learn...

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...

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, ...

Monotonic classification problems mean that both feature values and class labels are ordered and monotonicity relationships exist between some features and the decision label. Extreme Learning Machine (ELM) is a singlehidden layer feedforward neural network with fast training rate and good generalization capability, but due to the existence of training error, ELM cannot be directly used to hand...

Unlike the conventional neural network theories and implementations, Huang et al. [Universal approximation using incremental constructive feedforward networks with random hidden nodes, IEEE Transactions on Neural Networks 17(4) (2006) 879–892] have recently proposed a new theory to show that single-hidden-layer feedforward networks (SLFNs) with randomly generated additive or radial basis functi...