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
Hierarchical ensemble of Extreme Learning Machine
TL;DR: Results are compared with existing methods for 22 classification problems, showing that HE-ELM is able to achieve significant improvement in terms of classification accuracy, with a reduced risk of overfitting the training data.
Journal ArticleDOI
An enhance excavation equipments classification algorithm based on acoustic spectrum dynamic feature
TL;DR: An improved algorithm based on the popular extreme learning machine (ELM) is proposed for classifier learning and the leave-one-out cross validation strategy is adopted for the regularization parameter optimization in ELM.
Journal ArticleDOI
On the construction of extreme learning machine for online and offline one-class classification—An expanded toolbox
TL;DR: This paper presents six OCC methods and their thirteen variants based on extreme learning machine (ELM) and online sequential ELM (OSELM), and intends to expand the functionality of the most used toolbox for OCC i.e. DD toolbox.
Journal ArticleDOI
Fast dimensionality reduction and classification of hyperspectral images with extreme learning machines
TL;DR: A new real-time method for dimensionality reduction and classification of hyperspectral images that exploits artificial neural networks used to develop a fast compressor based on the extreme learning machine is presented.
Journal ArticleDOI
Classification Based on Multilayer Extreme Learning Machine for Motor Imagery Task from EEG Signals
TL;DR: A classification system based on Multilayer Extreme Learning Machine (ML-ELM), where the combination of PCA and LDA is chosen as the method of feature extraction and the ML- ELM is used to classify.
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.
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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.