This paper presents an analysis of the recently proposed sparse Extreme Learning Machine (S-ELM) classifier and describes an optimization scheme that can be used to calculate the network output weights. This optimization scheme exploits intrinsic graph structures in order to describe geometric data relationships in the so-called ELM space. Kernel formulations of the approach operating in ELM sp...

Uncertainty quantification is crucial to assess prediction quality of a machine learning model. In the case Extreme Learning Machines (ELM), most methods proposed in literature make strong assumptions on data, ignore randomness input weights or neglect bias contribution confidence interval estimations. This paper presents novel estimations that overcome these constraints and improve understandi...

In recent years, some deep learning methods have been developed and applied to image classification applications, such as convolutional neuron network (CNN) and deep belief network (DBN). However they are suffering from some problems like local minima, slow convergence rate, and intensive human intervention. In this paper, we propose a rapid learning method, namely, deep convolutional extreme l...

In recent years, ESG (Environmental, Social, and Governance) has become a critical indicator for evaluating sustainable companies. However, the actual logic used score calculation remains exclusive to rating agencies. Therefore, with advancement of AI, using machine learning establish reliable prediction model is topic worth exploring. This study aims build models non-financial industry in Taiw...

Aiming at the problem that the three-phase APF’s dynamic model is a multi-variable, nonlinear and strong coupling system, an internal model controller for three-phase APF based on LS-Extreme Learning Machine is studied in this paper. As a novel single hidden layer feed-forward neural networks, extreme learning machine (ELM) has several advantages: simple net structural, fast learning speed, goo...

Extreme learning machine (ELM) has gained increasing interest from various research fields recently. In this review, we aim to report the current state of the theoretical research and practical advances on this subject. We first give an overview of ELM from the theoretical perspective, including the interpolation theory, universal approximation capability, and generalization ability. Then we fo...

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...

School of Information and Engineering, Ocean University of China, Shandong, Qingdao, China 266000 School of mechanical and Electrical Engineering, China Jiliang University, Zhejiang, Hangzhou, China 310018 Department of Mechanical and Industrial Engineering and the Iowa Informatics Initiative, The University of Iowa, Iowa City, IA 52242-1527, USA Arcada University of Applied Sciences, 00550 Hel...