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
One-dimension hierarchical local receptive fields based extreme learning machine for radar target HRRP recognition
TL;DR: A one-dimension local receptive fields based extreme learning auto-encoder (1D ELM-LRF-AE) network for HRRP local structures and meaningful representations learning and recognition is proposed.
Journal ArticleDOI
Elongation Prediction of Steel-Strips in Annealing Furnace with Deep Learning via Improved Incremental Extreme Learning Machine
TL;DR: Wang et al. as mentioned in this paper proposed a deep architectures called I-ELM/MLCSA autoencoders with the concept of stacked generalization philosophy to solve large and complex data mining problems.
Book ChapterDOI
Deformable Surface Registration with Extreme Learning Machines
TL;DR: The proposed ELM model is trained to distinguish pairs of corresponding points from non-corresponding ones on the dataset with highly non-isometric distortions between models and the results show the increase of accuracy in surface registration task, and also reveal the bottleneck of the state-of-the-art shape descriptors.
Proceedings ArticleDOI
Evaluation of the Extreme Learning Machine for automatic fault diagnosis of the Tennessee Eastman chemical process
TL;DR: This work evaluated this classifier architecture in the context of automatic fault diagnosis to suggest that the Extreme Learning Machine is an attractive alternative classification method of process conditions.
Proceedings ArticleDOI
On the importance of pair-wise feature correlations for image classification
TL;DR: It is shown that simple linear classification of pairwise products of convolutional features achieves near state-of-the-art performance on some standard labelled image databases and provides insight on why `extreme-learning machines' can achieve classification performance equal to or better than the use of backpropagation training.
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.