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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Journal ArticleDOI
Self-adaptive Extreme Learning Machine Optimized by Rough Set Theory and Affinity Propagation Clustering
TL;DR: Empirical study of SELM algorithm on several commonly used classification benchmark problems shows that the proposed algorithm can find the proper number of hidden nodes and construct compact network classifiers, comparing with traditional ELM algorithm.
Journal ArticleDOI
The Evolution of Neural Learning Systems: A Novel Architecture Combining the Strengths of NTs, CNNs, and ELMs
TL;DR: An overview of the mainstream brain-inspired architectures and research directions proposed over the past decade is provided and a novel architecture exploiting the strengths of the current methods is proposed.
Proceedings ArticleDOI
Extreme learning machines in the field of text classification
TL;DR: Results show that ELM based classifiers can outperform many of the traditional classification techniques including the most powerful state-of-the-art technique such as SVM.
Journal ArticleDOI
Sensitive time series prediction using extreme learning machine
TL;DR: The simulations show that the suggested ST-ELM can improve the existing performance when dealing with the idle spectrum prediction of cognitive wireless network and the efficiency of sensitive time series using Extreme Learning Machine is examined.
Journal ArticleDOI
An effective hierarchical extreme learning machine based multimodal fusion framework
TL;DR: This paper introduces an ELM based hierarchical framework for multimodal data that obtains faster convergence and achieves better classification performance compared with the other existing multi-modal deep learning models.
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.