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
More filters
Proceedings ArticleDOI
Deep semi-supervised learning using Multi-Layered Extreme Learning Machines
TL;DR: The problem of extending nonlinear embedding algorithms to Multi-Layered Extreme Learning Machines by plugging the network to auxiliary layers to improve the semi-supervised learning performance by further building on the structure assumption of data is solved.
Dissertation
Extreme learning machine using filters for artificial lateral line source localisation
TL;DR: Data of an artificial lateral line, an array of sensors which are able to sense the differences in water flow, can be used for source localisation and angle prediction, and possibilities to improve an extreme learning machine used for this purpose are explored.
Proceedings ArticleDOI
Tree-line Contradictory Risk Early Warning Technology of Rural Distribution Network Considering Severe Convective Weather
TL;DR: In this paper , a zonal early warning mechanism for tree-line contradiction of rural distribution networks considering severe convective weather is proposed, where the Synthetic Minority Oversampling Technique (SMOTE) algorithm is used to replace part of the majority class samples with minority class samples, and the data preprocessing is realized based on keeping the scale of the data set unchanged.
Journal ArticleDOI
Writer verification based on simple graphemes and extreme learning machine approaches
A. Vásquez,M. Mora,V. Aubin,E. Salazar,Ricardo J. Barrientos,René Landero Hernández,K. Vilches +6 more
Proceedings ArticleDOI
Facial Expression Classification and Recognition Based on Improved Hybrid CNN-ELM Model
TL;DR: This model uses convolutional neural network to learn convolution features of facial expressions, and feeds them to the extreme learning machine (ELM) for face expression classification and recognition.
References
More filters
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.