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Search · four archives
19 papers · ranked by Valyu relevance
Longbin Lu, Xinman Zhang, Xuebin Xu, Zhiqiang Cai
An extreme learning machine (ELM) is a novel training method for single-hidden layer feedforward neural networks (SLFNs) in which the hidden nodes are randomly assigned and fixed without iterative tuning. ELMs have earned widespread global interest due to their fast learning speed, satisfactory generalization ability…
Hui-yuan Tian, Shi-jian Li, Tian-qi Wu, Min Yao
Extreme learning machine algorithm proposed in recent years has been widely used in many fields due to its fast training speed and good generalization performance. Unlike the traditional neural network, the ELM algorithm greatly improves the training speed by randomly generating the relevant parameters of the input…
Debendra Muduli, Santosh Kumar Sharma, Sujata Dash, Bernardo Lemos + 1 more
Glaucoma is a prominent threat to vision and ranks as the third leading cause of blindness in India. Early detection is crucial to limit its progression. Retinal image analysis, particularly computer-aided diagnosis (CAD), has gained significant attention due to its potential in effectively screening for and managing…
Yinggao Yue, Li Cao, Haishao Chen, Yaodan Chen + 4 more
'Heming Jia' 'Laith Abualigah' 'Xuewen Xia'] The features of the kernel extreme learning machine-efficient processing, improved performance, and less human parameter setting-have allowed it to be effectively used to batch multi-label classification tasks. These classic classification algorithms must at present contend…
Shan Pang, Xinyi Yang
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…
Apdullah Yayık, Yakup Kutlu, Gökhan Altan
Brain-computer interfaces (BCIs) aim to provide neuroscientific communication platform for human-beings, in particular locked-in patients. In most cases event-related potentials (ERPs), averaged voltage responses to a specific target stimuli over time, have key roles in designing BCIs. With this reason, for the last…
Caglar Uyulan
Human-machine interfaces contribute to the improvement of the life quality of physically disabled users. In this study, a non-invasive brain-machine interface (BMI) design methodology was proposed to control a robot arm through magnetoencephalography (MEG) based on directionally modulated MEG activity that was acquired…
Mark D. McDonnell, Migel D. Tissera, André van Schaik, Jonathan Tapson
'Jonathan Tapson'] Recent advances in training deep (multi-layer) architectures have inspired a renaissance in neural network use. For example, deep convolutional networks are becoming the default option for difficult tasks on large datasets, such as image and speech recognition. However, here we show that error rates…
Dibyasundar Das, Deepak Ranjan Nayak, Ratnakar Dash, Banshidhar Majhi
'Banshidhar Majhi'] Abstract—The extreme learning machine needs a large number of hidden nodes to generalize a single hidden layer neural network for a given training data-set. The need for more number of hidden nodes suggests that the neural-network is memorizing rather than generalizing the model. Hence, a supervised…
Sen Zhang, Tao Zhang, Yixin Yin, Wendong Xiao
The concentration of alumina in the electrolyte is of great significance during the production of aluminum. The amount of the alumina concentration may lead to unbalanced material distribution and low production efficiency and affect the stability of the aluminum reduction cell and current efficiency. The existing…
A. Pacheco, Renato A. Krohling, Carlos B. da Silva
The Extreme Learning Machine (ELM) is a single-hidden layer feedforward neural network (SLFN) learning algorithm that can learn effectively and quickly. The ELM training phase assigns the input weights and bias randomly and do not change them in the whole process. Although the network works well, the random weights in…
Shaofei Zang, Xinghai Li, Jianwei Ma, Yongyi Yan + 2 more
'Yuan Wei'] As a single-layer feedforward network (SLFN), extreme learning machine (ELM) has been successfully applied for classification and regression in machine learning due to its faster training speed and better generalization. However, it will perform poorly for domain adaptation in which the distributions…
Hernandez-Hernandez, Rolando A., Rubio-Solis, Adrian
Multilayer Extreme Learning Machine (ML-ELM) and its variants have proven to be an effective technique for the classification of different natural signals such as audio, video, acoustic and images. In this paper, a Hybrid Multilayer Extreme Learning Machine (HML-ELM) that is based on ELMbased autoencoder (ELM-AE) and…
Athanasios Vlontzos
In this paper we examine learning methods combining the Random Neural Network, a biologically inspired neural network and the Extreme Learning Machine that achieve state of the art classification performance while requiring much shorter training time. The Random Neural Network is a integrate and fire computational…
Naxian Ni, Suchuan Dong
The extreme learning machine (ELM) method can yield highly accurate solutions to linear/nonlinear partial differential equations (PDEs), but requires the last hidden layer of the neural network to be wide to achieve a high accuracy. If the last hidden layer is narrow, the accuracy of the existing ELM method will be…
J. Meiyazhagan, S. Sudharsan, A. Venkatesan, M. Senthilvelan
Machine learning models play a vital role in the prediction task in several fields of study. In this work, we utilize the ability of machine learning algorithms to predict the occurrence of extreme events in a nonlinear mechanical system. Extreme events are rare events that occur ubiquitously in nature. We consider…
Pablo Millán Arias, Joseph Butler, Gurjit S. Randhawa, Maximillian P. M. Soltysiak + 2 more
This study provides comprehensive quantitative evidence suggesting that adaptations to extreme temperatures and pH imprint a discernible environmental component in the genomic signature of microbial extremophiles. Both supervised and unsupervised machine learning algorithms were used to analyze genomic signatures, each…
Authors not listed
The performance of electrochemical cells for energy storage and conversion, such as batteries and fuel cells, can be improved by optimizing their manufacturing processes. This can be very time consuming and costly through the conventional trial-and-error approaches. Machine Learning (ML) models can help to accelerate…
Gabriele Di Antonio, Tommaso Gili, Andrea Gabrielli, Maurizio Mattia
Exploring the dynamics of a complex system, such as the human brain, poses significant challenges due to inherent uncertainties and limited data. In this study, we enhance the capabilities of noisy linear recurrent neural networks (lRNNs) within the reservoir computing framework, demonstrating their effectiveness in…