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
Experimental Study on Extreme Learning Machine Applications for Speech Enhancement
TL;DR: The experimental results indicate that when the amount of training data is limited, both ELM- and H-ELM-based speech enhancement techniques consistently outperform the conventional BP-based shallow and deep learning algorithms, in terms of standardized objective evaluations, under various testing conditions.
Journal ArticleDOI
Data Partition Learning With Multiple Extreme Learning Machines
TL;DR: This report offers an extreme learning machine (ELM)-based learning method that extends the ELM learning method from a single neural network to a multinetwork learning system, as the proposed multiELM method can approximate any target continuous function and classify disjointed regions.
Review of Advances in Neural Networks: Neural Design Technology Stack
TL;DR: This review provides a high-level synthesis of significant recent advances in artificial neural network research, as well as multi-disciplinary concepts connected to the far-reaching goal of obtaining intelligent systems.
Journal ArticleDOI
Convolutional neural network based on an extreme learning machine for image classification
Youngmin Park,Hyunseung Yang +1 more
TL;DR: The proposed training algorithm is a layer-wise training method for CNN, and uses an alternating strategy of random convolutional filters and semi-supervised filters to combine the advantages of both approaches.
Journal ArticleDOI
Learning Distributions of Image Features by Interactive Fuzzy Lattice Reasoning in Pattern Recognition Applications
TL;DR: This paper describes the recognition of image patterns based on novel representation learning techniques by considering higher-level (meta-)representations of numerical data in a mathematical lattice of (Type-1) Intervals' Numbers, where an IN represents a distribution of image features including orthogonal moments.
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.