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
Extreme learning machine via free sparse transfer representation optimization
TL;DR: Comprehensive experiments show that TFSR-based algorithms outperform the existing transfer learning methods and are robust to different sizes of training data.
Journal ArticleDOI
Odor Recognition with a Spiking Neural Network for Bioelectronic Nose.
TL;DR: A spiking neural network (SNN)-based odor recognition method from spike trains recorded by the implanted electrode array with a voltage-based regulation strategy that achieves about 15% improvement compared with a classical SNN model.
Book ChapterDOI
The Random Neural Network with a Genetic Algorithm and Deep Learning Clusters in Fintech: Smart Investment
TL;DR: The Random Neural Network in a Deep Learning Cluster structure with a new learning algorithm based on the genetics according to the genome model, where information is transmitted in the combination of genes rather than the genes themselves, is presented.
Proceedings ArticleDOI
A Novel Deep Learning Approach: Stacked Evolutionary Auto-encoder
TL;DR: SEvoAE method based on using EMO algorithm to train single layer auto-encoder, and sequentially learning deeper representation in a stacking way is presented, showing that the proposed method is able to outperform existing methods with a reduced risk of overfitting the training data.
Journal ArticleDOI
Spectral regression based marginal Fisher analysis dimensionality reduction algorithm
TL;DR: It is proved that the proposed marginal FisherAnalysis criterion based on extreme learning machine (ELM) is a special case of traditional kernel marginal Fisher analysis, and a novel supervised dimensionality reduction algorithm is presented by virtue of ELM and spectral regression.
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.