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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Proceedings ArticleDOI
Deep neural network acceleration framework under hardware uncertainty
TL;DR: A general framework called FramNN is proposed, which adjusts DNN training model to make it appropriate for underlying hardware to accelerate training and applies adaptive approximation which dynamically changes the level of hardware approximation depending on the DNN error rate.
Book ChapterDOI
Deep Learning in IoT: Introduction, Applications, and Perspective in the Big Data Era
G Devika,Asha Gowda Karegowda +1 more
TL;DR: This chapter provides a detailed account of the IoT domain, machine learning, and DL techniques and applications and current challenges and potential areas for future research.
Journal ArticleDOI
Feature learning for stacked ELM via low-rank matrix factorization
TL;DR: An improved ELM-AE architecture is proposed which utilize low-rank matrix factorization to learn optimal low-dimensional features and enhances features nonlinear ability, since features are learned directly from the nonlinear outputs of hidden layer.
Journal ArticleDOI
Modeling and forecasting the electricity clearing price: A novel BELM based pattern classification framework and a comparative analytic study on multi-layer BELM and LSTM
TL;DR: The findings suggest that the proposed pattern classification framework can obtain satisfactory forecasting results provided that suitable scheme is utilized to the pattern segmentation and feature ranking process.
Journal ArticleDOI
A review of Computational Intelligence techniques in coral reef-related applications
TL;DR: The most commonly used CI techniques related to coral reefs are described and the main improvements obtained with these methods over classical algorithms in this field are described.
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.