Results 11 to 20 of about 599,706 (282)
Functional extreme learning machine [PDF]
Frontiers in Computational Neuroscience, 2023 IntroductionExtreme learning machine (ELM) is a training algorithm for single hidden layer feedforward neural network (SLFN), which converges much faster than traditional methods and yields promising performance.
Xianli Liu +6 more
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Graph Embedded Extreme Learning Machine [PDF]
IEEE Transactions on Cybernetics, 2016 In this paper, we propose a novel extension of the extreme learning machine (ELM) algorithm for single-hidden layer feedforward neural network training that is able to incorporate subspace learning (SL) criteria on the optimization process followed for the calculation of the network's output weights. The proposed graph embedded ELM (GEELM) algorithm is
Pitas, Ioannis +2 more
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Energies, 2022 Due to the advantages of high convergence accuracy, fast training speed, and good generalization performance, the extreme learning machine is widely used in model identification. However, a gas turbine is a complex nonlinear system, and its sampling data
Rui Yang +3 more
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Time efficient variants of Twin Extreme Learning Machine
Intelligent Systems with Applications, 2023 Twin Extreme Learning Machine models can obtain better generalization ability than the standard Extreme Learning Machine model. But, they require to solve a pair of quadratic programming problems for this.
Pritam Anand +2 more
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Incremental extreme learning machine [PDF]
2018 22nd International Computer Science and Engineering Conference (ICSEC), 2019 This new theory shows that in order to let SLFNs work as universal approximators, one may simply randomly choose input-to-hidden nodes, and then we only need to adjust the output weights linking the hidden layer and the output layer. In such SLFNs implementations, the activation functions for additive nodes can be any bounded nonconstant piecewise ...
Boonnithi Jiramaneepinit +1 more
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Dual-Weighted Kernel Extreme Learning Machine for Hyperspectral Imagery Classification
Remote Sensing, 2021 Due to its excellent performance in high-dimensional space, the kernel extreme learning machine has been widely used in pattern recognition and machine learning fields.
Xumin Yu +4 more
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Multilayer Fisher extreme learning machine for classification
Complex & Intelligent Systems, 2022 As a special deep learning algorithm, the multilayer extreme learning machine (ML-ELM) has been extensively studied to solve practical problems in recent years.
Jie Lai +4 more
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Binary/ternary extreme learning machines [PDF]
Neurocomputing, 2015 In this paper, a new hidden layer construction method for Extreme Learning Machines (ELMs) is investigated, aimed at generating a diverse set of weights. The paper proposes two new ELM variants: Binary ELM, with a weight initialization scheme based on { 0 , 1 } -weights; and Ternary ELM, with a weight initialization scheme based on { - 1 , 0 , 1 ...
van Heeswijk, Mark, Miche, Yoan
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Evolutionary Cost-Sensitive Extreme Learning Machine [PDF]
IEEE Transactions on Neural Networks and Learning Systems, 2017 Conventional extreme learning machines solve a Moore-Penrose generalized inverse of hidden layer activated matrix and analytically determine the output weights to achieve generalized performance, by assuming the same loss from different types of misclassification. The assumption may not hold in cost-sensitive recognition tasks, such as face recognition
Lei Zhang, David Zhang
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Optimized Extreme Learning Machine
International Journal for Research in Applied Science and Engineering Technology, 2022 Abstract: Extreme Learning Machine (ELM) is a learning method for single-hidden layer feedforward neural network (SLFN) training. The ELM strategy speeds up learning by generating input weights and biases for hidden nodes at random rather than modifying network parameters, making it much faster than the standard gradient-based approach. In this project,
Roshan Kaloni +3 more
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