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
Multilayer probability extreme learning machine for device-free localization
TL;DR: A novel hierarchical ELM based on deep learning theory, named multilayer probability ELM (MP-ELM), for automatically extracting the useful information from the links, and implementing fast and accurate DFL is proposed.
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A Review on Deep Learning Approaches for 3D Data Representations in Retrieval and Classifications
TL;DR: It can be concluded that deep learning together with a suitable 3D data representation gives an effective approach for improving the performance of 3D shape analysis.
Journal ArticleDOI
Convolutional sparse coding-based deep random vector functional link network for distress classification of road structures
TL;DR: The construction of a novel neural network, CSDRN, by the use of a combination of CSC‐based feature extraction and the DRN classifier, which can also be trained from a small dataset, is shown in this paper.
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Study on suitability and importance of multilayer extreme learning machine for classification of text data
TL;DR: This study proposes an efficient technique that uses the concept of connected component (CC) of a graph and Wordnet along with four established feature selection techniques to select the best features from a given input dataset in order to prepare an efficient training feature vector.
Journal ArticleDOI
Weighted Tanimoto Extreme Learning Machine with Case Study in Drug Discovery
TL;DR: Experiments show that T-WELMs achieve much better classification results and are at the same time faster in terms of both training time and further classification than both ELM models and other state-of-the-art methods in the field.
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.
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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.