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
Numerical solution for high-order ordinary differential equations using H-ELM algorithm
Yanfei Lu,Futian Weng,Hongli Sun +2 more
TL;DR: Experiments indicate that the H-ELM model achieves much higher accuracy, lower complexity but stronger generalization ability than existed methods and could be a good tool to solve higher order linear ODEs and higher orderlinear SODEs.
Book ChapterDOI
Improving the Speed and Quality of Extreme Learning Machine by Conjugate Gradient Method
TL;DR: A novel approach based on Conjugate Gradient Method (CG) is proposed in this Article to improve original ELM, and is both faster and have higher quality on all tested datasets.
Journal ArticleDOI
Training Autoencoders Using Relative Entropy Constraints
Yanjun Li,Yongquan Yan +1 more
TL;DR: In this paper , a relative entropy autoencoder (REAE) is proposed to solve the feature map parameters, which imposes different constraints on the average activation value of the hidden layer outputs obtained by feature map for different data sets.
Book ChapterDOI
Evaluating Confidence Intervals for ELM Predictions
TL;DR: The proposed method computed particular confidence intervals for each data sample to make ELM predictions more intuitive to interpret, and an ELM model more applicable in practice under task-specific requirements.
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.