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1. Full Text Convex incremental extreme learning machine
by Huang, Guang-Bin and Chen, Lei
Neurocomputing, ISSN 0925-2312, 2007, Volume 70, Issue 16, pp. 3056 - 3062
Unlike the conventional neural network theories and implementations, Huang et al. [Universal approximation using incremental constructive feedforward networks... 
Convergence rate | Generalized feedforward networks | Random hidden nodes | Incremental extreme learning machine | Convex optimization | generalized feedforward networks | incremental extreme learning machine | NEURAL-NETWORKS | FUNCTION APPROXIMATION | convex optimization | MULTILAYER FEEDFORWARD NETWORKS | convergence rate | random hidden nodes | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | Neural networks | Algorithms
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2. Full Text Extreme learning machines: a survey
by Huang, Guang-Bin and Wang, Dian Hui and Lan, Yuan
International Journal of Machine Learning and Cybernetics, ISSN 1868-8071, 6/2011, Volume 2, Issue 2, pp. 107 - 122
Computational intelligence techniques have been used in wide applications. Out of numerous computational intelligence techniques, neural networks and support... 
Extreme learning machine | Online sequential learning | Support vector machine | Statistical Physics, Dynamical Systems and Complexity | ELM feature space | Systems Biology | ELM kernel | Engineering | Pattern Recognition | Incremental learning | Computational Intelligence | Control, Robotics, Mechatronics | Ensemble | Artificial Intelligence (incl. Robotics) | REGRESSION | APPROXIMATION | ALGORITHM | CLASSIFICATION | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | BOUNDS | NEURAL-NETWORK | FEEDFORWARD NETWORKS | HIDDEN NEURONS
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3. Full Text Enhanced random search based incremental extreme learning machine
by Huang, Guang-Bin and Chen, Lei
Neurocomputing, ISSN 0925-2312, 2008, Volume 71, Issue 16, pp. 3460 - 3468
Recently an incremental algorithm referred to as incremental extreme learning machine (I-ELM) was proposed by Huang et al. [G.-B. Huang, L. Chen, C.-K. Siew,... 
Convergence rate | Random hidden nodes | Incremental extreme learning machine | Random search | NEURAL-NETWORKS | FUNCTION APPROXIMATION | FEEDFORWARD NETWORKS | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | Searches and seizures | Algorithms
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4. Full Text Sparse pseudoinverse incremental extreme learning machine
by Kassani, Peyman Hosseinzadeh and Teoh, Andrew Beng Jin and Kim, Euntai
Neurocomputing, ISSN 0925-2312, 04/2018, Volume 287, pp. 128 - 142
An extreme learning machine (ELM) is a popular analytic single hidden layer feedforward neural network because of its rapid learning capacity. However, vanilla... 
Hidden neuron | Extreme learning machine | Sparsity | Matrix decomposition | Least squares estimation | HIDDEN-NODES | UNIVERSAL APPROXIMATION | GROWTH | CLASSIFICATION | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
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5. Full Text Bankruptcy prediction using Extreme Learning Machine and financial expertise
by Yu, Qi and Miche, Yoan and Séverin, Eric and Lendasse, Amaury
Neurocomputing, ISSN 0925-2312, 03/2014, Volume 128, pp. 296 - 302
Bankruptcy prediction has been widely studied as a binary classification problem using financial ratios methodologies. In this paper, Leave-One-Out-Incremental... 
Leave-One-Out | Extreme Learning Machine | Incremental Learning | Bankruptcy Prediction | RATIOS | NEURAL-NETWORKS | CLASSIFICATION | FAILURE | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | Computer science | Analysis | Bankruptcy | Learning | Tasks | Neurons | Classification | Strategy | Bankruptcies | Indicators | Plugs
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6. Full Text Elastic extreme learning machine for big data classification
by Xin, Junchang and Wang, Guoren and Wang, Zhiqiong and Qu, Luxuan
Neurocomputing, ISSN 0925-2312, 02/2015, Volume 149, pp. 464 - 471
Extreme Learning Machine (ELM) and its variants have been widely used for many applications due to its fast convergence and good generalization performance.... 
Incremental learning | Correctional learning | Extreme learning machine | Big data classification | Decremental learning | REGRESSION | ELM | FRAMEWORK | FEEDFORWARD NETWORKS | MAPREDUCE | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | Analysis | Big data | Learning | Training | Multiplication | Tasks | Computation | Neural networks | Mathematical analysis | Shortages | Convergence
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7. Full Text Universal Approximation of Extreme Learning Machine With Adaptive Growth of Hidden Nodes
by Rui Zhang and Yuan Lan and Guang-Bin Huang and Zong-Ben Xu
IEEE Transactions on Neural Networks and Learning Systems, ISSN 2162-237X, 02/2012, Volume 23, Issue 2, pp. 365 - 371
Extreme learning machines (ELMs) have been proposed for generalized single-hidden-layer feedforward networks which need not be neuron-like and perform well in... 
Learning systems | Training | Extreme learning machine (ELM) | growing algorithm | Adaptive systems | Additives | feedforward neural networks | incremental learning | Machine learning | universal approximation | Function approximation | COMPUTER SCIENCE, HARDWARE & ARCHITECTURE | NEURAL-NETWORKS | MULTILAYER FEEDFORWARD NETWORKS | COMPUTER SCIENCE, THEORY & METHODS | ALGORITHMS | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | ENGINEERING, ELECTRICAL & ELECTRONIC | Algorithms | Feedback | Artificial Intelligence | Computer Simulation | Models, Statistical | Nonlinear Dynamics | Pattern Recognition, Automated - methods | Neural Networks (Computer) | Technology application | Usage | Neural networks | Approximation theory | Regression analysis | Design and construction | Methods | Simulation methods | Mathematical functions | Learning | Networks | Silver | Approximation | Computer simulation | Regression | Frozen
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8. Full Text Constructive hidden nodes selection of extreme learning machine for regression
by Lan, Yuan and Soh, Yeng Chai and Huang, Guang-Bin
Neurocomputing, ISSN 0925-2312, 2010, Volume 73, Issue 16, pp. 3191 - 3199
In this paper, we attempt to address the architectural design of ELM regressor by applying a constructive method on the basis of ELM algorithm. After the... 
Error-minimized extreme learning machine | Constructive method | Incremental extreme learning machine | Extreme learning machine | NEURAL-NETWORKS | LEAST ANGLE REGRESSION | ALGORITHM | FEEDFORWARD NETWORKS | IDENTIFICATION | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | BASIS FUNCTION NETWORKS | Analysis | Algorithms | Learning | Networks | Construction | Construction equipment | Elm | Regression | Optimization | Neural networks
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9. Full Text An incremental extreme learning machine for online sequential learning problems
by Guo, Lu and Hao, Jing-hua and Liu, Min
Neurocomputing, ISSN 0925-2312, 03/2014, Volume 128, pp. 50 - 58
A fast and outstanding incremental learning algorithm is required to meet the demand of online applications where data comes one by one or chunk by chunk to... 
Extreme learning machine (ELM) | Fixed size LSSVM (FS-LSSVM) | Online sequential ELM (OS-ELM) | Incremental learning algorithm | Incremental ELM (IELM) | CLASSIFIER | NETWORKS | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | Data mining | Analysis | Algorithms | Learning | Demand | Semiconductors | Neural networks | Mathematical models | Retraining | Online
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10. Full Text Timeliness online regularized extreme learning machine
by Luo, Xiong and Yang, Xiaona and Jiang, Changwei and Ban, Xiaojuan
International Journal of Machine Learning and Cybernetics, ISSN 1868-8071, 3/2018, Volume 9, Issue 3, pp. 465 - 476
To improve the learning performance, a novel online sequential extreme learning machine (ELM) algorithm for single-hidden layer feedforward networks is... 
Engineering | Pattern Recognition | Incremental learning | Computational Intelligence | Extreme learning machine | Timeliness online regularized extreme learning machine | Control, Robotics, Mechatronics | Complex Systems | Artificial Intelligence (incl. Robotics) | Systems Biology | Regularization | REGRESSION | ALGORITHM | CLASSIFICATION | NETWORKS | MODEL | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
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11. Full Text Incremental regularized extreme learning machine and it's enhancement
by Xu, Zhixin and Yao, Min and Wu, Zhaohui and Dai, Weihui
Neurocomputing, ISSN 0925-2312, 01/2016, Volume 174, pp. 134 - 142
Extreme Learning Machine (ELM) proposed by Huang et al. [2] is a novel algorithm for single hidden layer feedforward neural networks (SLFNs) with extremely... 
Incremental learning | Extreme Learning Machine | Neural networks | Regularization | REGRESSION | NEURAL-NETWORKS | CLASSIFICATION | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | Algorithms
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12. Full Text Incremental laplacian regularization extreme learning machine for online learning
by Yang, Shuyuan and Yang, Lixia and Li, Sujing and Liu, Zhi and Jiao, Licheng
Applied Soft Computing, ISSN 1568-4946, 10/2017, Volume 59, pp. 546 - 555
The past decade has witnessed an explosive growth of data streams in Internet, biometrics, remote sensing and other fields. Nowadays many supervised... 
Extreme learning machine | Semi-supervised incremental/online learning | Graph laplacian | Incremental laplacian regularization | COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | Electrical engineering | Online education | Data mining | Machine learning
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13. Full Text Modified bidirectional extreme learning machine with Gram–Schmidt orthogonalization method
by Zeng, Guoqiang and Zhang, Baihai and Yao, Fenxi and Chai, Senchun
Neurocomputing, ISSN 0925-2312, 11/2018, Volume 316, pp. 405 - 414
Incremental extreme learning machine has been proved to be an efficient and simple universal approximator. However, the network architecture may be very large... 
Orthogonalization method | Universal approximator | Inefficient nodes | Incremental extreme learning machine | Least square solution | NUMBER | APPROXIMATION | MULTILAYER FEEDFORWARD NETWORKS | CLASSIFICATION | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | NONLINEAR-SYSTEMS | HIDDEN NEURONS | ADAPTIVE NEURAL-CONTROL
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14. Full Text Gram–Schmidt process based incremental extreme learning machine
by Zhao, Yong-Ping and Li, Zhi-Qiang and Xi, Peng-Peng and Liang, Dong and Sun, Liguo and Chen, Ting-Hao
Neurocomputing, ISSN 0925-2312, 06/2017, Volume 241, pp. 1 - 17
To compact the architecture of extreme learning machine (ELM), two incremental learning algorithms are proposed in this paper. The previous incremental... 
Incremental learning | Gram–Schmidt process | Extreme learning machine | QR decomposition | SCHEME | Gram-Schmidt process | HIDDEN NODES | ALGORITHM | FEEDFORWARD NEURAL-NETWORKS | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | Algorithms | Electric power systems | Data mining | Neural networks
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15. Full Text Length-Changeable Incremental Extreme Learning Machine
by You-Xi Wu Dong Liu He Jiang
计算机科学技术学报：英文版, ISSN 1000-9000, 2017, Volume 32, Issue 3, pp. 630 - 643
Extreme learning machine （ELM） is a learning algorithm for generalized single-hidden-layer feed-forward networks （SLFNs）. In order to obtain a suitable network... 
incremental extreme learning machine (I-ELM) | single-hidden-layer feed-forward network (SLFN) | random hidden node | universal approximation | convergence rate | COMPUTER SCIENCE, SOFTWARE ENGINEERING | COMPUTER SCIENCE, HARDWARE & ARCHITECTURE | NEURAL-NETWORKS | APPROXIMATION | FEEDFORWARD NETWORKS | Computer science | Data mining | Algorithms | Studies | Job rotation | Machine learning | Feedforward | Man machine interaction | Convergence
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16. Full Text Coal Quality Exploration Technology Based on an Incremental Multilayer Extreme Learning Machine and Remote Sensing Images
by Le, Ba Tuan and Xiao, Dong and Mao, Yachun and He, Dakuo and Xu, Jialiu and Song, Liang
IEEE Transactions on Geoscience and Remote Sensing, ISSN 0196-2892, 07/2019, Volume 57, Issue 7, pp. 4192 - 4201
This paper proposes a new coal quality exploration method that detects coal quality in coal mining areas and explores and monitors the distribution and change... 
Satellites | Atmospheric measurements | Coal | Coal quality exploration | Coal mining | remote sensing images | Carbon | Remote sensing | incremental multilayer extreme learning machine (IM-ELM) | Geologic measurements | REGRESSION | GEOCHEMISTRY & GEOPHYSICS | REMOTE SENSING | WUDA | CLASSIFICATION | IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY | RANDOM FOREST | ENGINEERING, ELECTRICAL & ELECTRONIC
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17. Full Text Collaborative extreme learning machine with a confidence interval for P2P learning in healthcare
by XIE, Rongjun and Khalil, Ibrahim and Badsha, Shahriar and Atiquzzaman, Mohammed
Computer Networks, ISSN 1389-1286, 02/2019, Volume 149, pp. 127 - 143
In modern e-healthcare systems, medical institutions can provide more reliable diagnoses by introducing Machine-Learning (ML)-based classifiers. These ML... 
Collaborative ELM | Incremental learning | P2P network | Smart healthcare | P2P Learning | Confidence interval | REGRESSION | COMPUTER SCIENCE, HARDWARE & ARCHITECTURE | CLOUD | COMPUTER SCIENCE, INFORMATION SYSTEMS | TELECOMMUNICATIONS | ONLINE | ENGINEERING, ELECTRICAL & ELECTRONIC | MODELS | Health care | Classifiers | Peer to peer computing | Labels | Patients | Model accuracy | Nodes | Confidence intervals | Medical centres | Accuracy | Neural networks | Collaboration | Machine learning | Mathematical models
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18. Full Text Regularization incremental extreme learning machine with random reduced kernel for regression
by Zhou, Zhiyu and Chen, Ji and Zhu, Zefei
Neurocomputing, ISSN 0925-2312, 12/2018, Volume 321, pp. 72 - 81
For regression tasks, the existing extreme learning machine (ELM) and kernel extreme learning machine (KELM) algorithms exhibit singularity and over-fitting... 
Regression | Incremental extreme learning machine | Regularization | Random reduced kernel | FRAMEWORK | CLASSIFICATION | ELM SELECTIVE ENSEMBLE | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | Information science | Algorithms | Wolves | Neurons | Mechanical engineering
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