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
Deep Extreme Learning Machine and Its Application in EEG Classification
Shifei Ding,Shifei Ding,Nan Zhang,Nan Zhang,Xinzheng Xu,Xinzheng Xu,Guo Lili,Guo Lili,Jian Zhang,Jian Zhang +9 more
TL;DR: Effectiveness of the application of DELM in EEG classification is confirmed and it is confirmed that MLELM approximate the complicated function but it also does not need to iterate during the training process.
Journal ArticleDOI
Evolutionary Cost-Sensitive Extreme Learning Machine
Lei Zhang,David Zhang +1 more
TL;DR: This paper proposes an evolutionary cost-sensitive ELM, which to the best of the authors' knowledge, is the first proposal of ELM in evolutionary Cost-sensitive classification scenario, and well addresses the open issue of how to define the cost matrix in cost- sensitive learning tasks.
Journal ArticleDOI
Stacked Extreme Learning Machines
TL;DR: A stacked ELMs (S-ELMs) that is specially designed for solving large and complex data problems and can achieve much better testing accuracy than SVM and slightly better accuracy than deep belief network (DBN) with much faster training speed is proposed.
Journal ArticleDOI
Generalized extreme learning machine autoencoder and a new deep neural network
TL;DR: A new variant of extreme learning machine autoencoder (ELM-AE) called generalized extreme learning Machine Autoencoding (GELM) which adds the manifold regularization to the objective of ELM- AE is proposed which outperforms some state-of-the-art unsupervised learning algorithms.
Journal ArticleDOI
A Parallel Multiclassification Algorithm for Big Data Using an Extreme Learning Machine
TL;DR: An efficient ELM based on the Spark framework (SELM), which includes three parallel subalgorithms, is proposed for big data classification and implemented, which strengthens the learning ability of the SELM.
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.