Journal ArticleDOI
Representational learning with ELMs for big data
TLDR
Huang et al. as mentioned in this paper proposed ELM-AE, a special case of ELM, where the input is equal to output, and the randomly generated weights are chosen to be orthogonal.Abstract:
Geoffrey Hinton and Pascal Vincent showed that a restricted Boltzmann machine (RBM) and auto-encoders (AE) could be used for feature engineering. These engineered features then could be used to train multiple-layer neural networks, or deep networks. Two types of deep networks based on RBM exist: the deep belief network (DBN)1 and the deep Boltzmann machine (DBM). Guang-Bin Huang and colleagues introduced the extreme learning machine (ELM) as an single-layer feed-forward neural networks (SLFN) with a fast learning speed and good generalization capability. The ELM for SLFNs shows that hidden nodes can be randomly generated. ELM-AE output weights can be determined analytically, unlike RBMs and traditional auto-encoders, which require iterative algorithms. ELM-AE can be seen as a special case of ELM, where the input is equal to output, and the randomly generated weights are chosen to be orthogonal.read more
Citations
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Book ChapterDOI
Deep Extreme Learning Machines for Classification
TL;DR: The network achieves comparable performance to an ELM with a single hidden layer with a size equal to the total number of hidden-layer neurons in the deep network, suggesting that the method can be applied to a resource-constrained hardware implementation to increase the network performance.
Book ChapterDOI
K-means and Wordnet Based Feature Selection Combined with Extreme Learning Machines for Text Classification
TL;DR: A k-means clustering based feature selection for text classification is proposed and the empirical results demonstrate the applicability, efficiency and effectiveness of the approach using ELM and ML-ELM as the classifiers over state-of-the-art classifiers.
Journal ArticleDOI
Improved extreme learning machine with AutoEncoder and particle swarm optimization for short-term wind power prediction
TL;DR: In this paper, the authors presented a wind power forecasting approach based on regularized extreme learning machine algorithm (R-ELM), particle swarm optimization method (PSO), and AutoEncoder network (AE) so-called AE-ORELM.
Journal ArticleDOI
Regularized ensemble neural networks models in the Extreme Learning Machine framework
Carlos Perales-González,Mariano Carbonero-Ruz,David Becerra-Alonso,Javier Pérez-Rodríguez,Francisco Fernández-Navarro +4 more
TL;DR: A hierarchical ensemble methodology that promotes diversity among the elements of an ensemble, explicitly through the loss function in the single-hidden-layer feedforward network version of ELM is proposed.
Proceedings ArticleDOI
A low-dimensional vector representation for words using an extreme learning machine
TL;DR: An efficient method for generating word embeddings that uses an auto-encoder architecture based on ELM that works on a word context matrix is proposed and the results are reported.
References
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Journal ArticleDOI
Extreme learning machine: Theory and applications
TL;DR: A new learning algorithm called ELM is proposed for feedforward neural networks (SLFNs) which randomly chooses hidden nodes and analytically determines the output weights of SLFNs which tends to provide good generalization performance at extremely fast learning speed.
Journal ArticleDOI
Extreme Learning Machine for Regression and Multiclass Classification
TL;DR: ELM provides a unified learning platform with a widespread type of feature mappings and can be applied in regression and multiclass classification applications directly and in theory, ELM can approximate any target continuous function and classify any disjoint regions.
Journal ArticleDOI
Universal approximation using incremental constructive feedforward networks with random hidden nodes
TL;DR: This paper proves in an incremental constructive method that in order to let SLFNs work as universal approximators, one may simply randomly choose hidden nodes and then only need to adjust the output weights linking the hidden layer and the output layer.
Journal ArticleDOI
Optimization method based extreme learning machine for classification
TL;DR: Under the ELM learning framework, SVM's maximal margin property and the minimal norm of weights theory of feedforward neural networks are actually consistent and ELM for classification tends to achieve better generalization performance than traditional SVM.