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2018
<p>Extreme Learning Machine (ELM) is a recently discovered way of training Single Layer Feed-forward Neural Networks with an explicitly given solution, which exists because the input weights and biases are generated randomly and never change. The method in general achieves performance comparable to Error Back-Propagation, but the training time is
Anton Akusok +6 more
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<p>Extreme Learning Machine (ELM) is a recently discovered way of training Single Layer Feed-forward Neural Networks with an explicitly given solution, which exists because the input weights and biases are generated randomly and never change. The method in general achieves performance comparable to Error Back-Propagation, but the training time is
Anton Akusok +6 more
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Extreme learning machine with errors in variables
World Wide Web, 2013Extreme learning machine (ELM) is widely used in training single-hidden layer feedforward neural networks (SLFNs) because of its good generalization and fast speed. However, most improved ELMs usually discuss the approximation problem for sample data with output noises, not for sample data with noises both in input and output values, i.e., error-in ...
Jianwei Zhao 0004 +2 more
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Robust incremental extreme learning machine
2014 13th International Conference on Control Automation Robotics & Vision (ICARCV), 2014Extreme Learning Machine (ELM) is a special single-hidden-layer feedforward neural networks with very fast learning speed and has attracted significant research attentions in recent years. The salient feature of ELM is that the input parameters can be randomly generated instead of being exhaustively tuned, and thus saving a great deal of computational ...
Zhifei Shao, Meng Joo Er, Ning Wang 0002
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Extreme learning machine: Theory and applications
Neurocomputing, 2006Abstract It is clear that the learning speed of feedforward neural networks is in general far slower than required and it has been a major bottleneck in their applications for past decades. Two key reasons behind may be: (1) the slow gradient-based learning algorithms are extensively used to train neural networks, and (2) all the parameters of the ...
Guang-Bin Huang +2 more
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Reinforcement Learning Based on Extreme Learning Machine
2012Extreme learning machine not only has the best generalization performance but also has simple structure and convenient calculation. In this paper, its merits are used for reinforcement learning. The use of extreme learning machine on Q function approximation can improve the speed of reinforcement learning.
Jie Pan +3 more
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On the kernel Extreme Learning Machine classifier
Pattern Recognition Letters, 2015Abstract In this paper, we discuss the connection of the kernel versions of the ELM classifier with infinite Single-hidden Layer Feedforward Neural networks and show that the original ELM kernel definition can be adopted for the calculation of the ELM kernel matrix for two of the most common activation functions, i.e., the RBF and the sigmoid ...
Alexandros Iosifidis +2 more
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Extreme Learning Machine for Multilayer Perceptron
IEEE Transactions on Neural Networks and Learning Systems, 2016Extreme learning machine (ELM) is an emerging learning algorithm for the generalized single hidden layer feedforward neural networks, of which the hidden node parameters are randomly generated and the output weights are analytically computed. However, due to its shallow architecture, feature learning using ELM may not be effective for natural signals ...
Jiexiong Tang +2 more
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Reduced Kernel Extreme Learning Machine
2013We present a fast and accurate algorithm–reduced kernel extreme learning machine (Reduced-KELM). It randomly selects a subset from given dataset, and uses \(\mathcal{K}(X,\tilde{X})\) in place of \(\mathcal{K}(X,X)\). The large scale kernel matrix with size of n×n is reduced to \(n\times \tilde{n} \), and the time-consuming computation for inversion of
Wanyu Deng, Qinghua Zheng, Kai Zhang
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Extreme Learning Machine – A New Machine Learning Paradigm
In neural network theory, we analyze two strategies for learning weights: backpropagation and Extreme Learning Machine. The former is common in ANNs with one or more hidden layers, while the latter is becoming popular in ANNs with exactly one hidden layer and weights chosen based on a random selection of the weights and biases of the hidden neurons.openaire +1 more source
Hierarchical extreme learning machines
Neurocomputing, 2018Guang-Bin Huang +2 more
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