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
The Random Neural Network with Deep Learning Clusters in Smart Search
TL;DR: A Deep Learning Cluster to perform as a Management Cluster that decides the final result relevance based on the inputs from each independent Deep Learning cluster is presented.
Journal ArticleDOI
Hyperspectral image classification based on average spectral-spatial features and improved hierarchical-ELM
Ba Tuan Le,Thai Thuy Lam Ha +1 more
TL;DR: The proposed method for hyperspectral images classification is better than other advanced classification methods in training time and accuracy and yields satisfactory results.
Journal ArticleDOI
Quantitative Analysis of Gas Phase IR Spectra Based on Extreme Learning Machine Regression Model.
Tinghui Ouyang,Chongwu Wang,Zhangjun Yu,Zhangjun Yu,Robert Stach,Boris Mizaikoff,Bo Liedberg,Guang-Bin Huang,Qi Jie Wang +8 more
TL;DR: An advanced regression model based on the extreme learning machine (ELM) algorithm for quantitative chemometric analysis based on infrared (IR) spectroscopic/sensing techniques is proposed to offer superior robustness and performance in quantitative IR spectral analysis.
Book ChapterDOI
Deep Learning in the Domain of Multi-Document Text Summarization
TL;DR: The problem of extractive text summarization which works by selecting a subset of phrases or sentences from the original document(s) to form a summary is addressed and the effectiveness of Multilayer ELM and its stability for usage is highlighted.
Journal ArticleDOI
Solving the ruin probabilities of some risk models with Legendre neural network algorithm
TL;DR: The results obtained by the proposed Legendre neural network model can achieve very high accuracy and is well suited for solving the ruin probabilities of the risk 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.