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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Book ChapterDOI
Explicit Computation of Input Weights in Extreme Learning Machines
TL;DR: A closed form expression for initializing the input weights in a multilayer perceptron, which can be used as the first step in synthesis of an Extreme Learning Machine.
Journal ArticleDOI
Imbalanced biomedical data classification using self-adaptive multilayer ELM combined with dynamic GAN.
TL;DR: A novel multilayer extreme learning machine(ELM) classification model combined with dynamic generative adversarial net (GAN) to tackle limited and imbalanced biomedical data and could offer a theoretical basis for computer-aided diagnosis.
Journal ArticleDOI
Radar emitter identification with bispectrum and hierarchical extreme learning machine
TL;DR: A novel method is proposed for radar emitter signal identification, where the bispectrum estimation of radar signal is extracted and the recent hierarchical extreme learning machine (BS + H-ELM) is adopted for further feature learning and recognition.
Proceedings ArticleDOI
Multi-layer Extreme Learning Machine-based Autoencoder for Hyperspectral Image Classification
TL;DR: A multi-layer extreme learning machine-based autoencoder (MLELM-AE) for HSI classification that not only maintains the fast speed of traditional ELM but also greatly improves the performance of H SI classification.
Journal ArticleDOI
Deep extreme learning machine with leaky rectified linear unit for multiclass classification of pathological brain images
TL;DR: The basic purpose of employing ML-ELM+LReLU algorithm is to eliminate the need for hand-crafted feature extraction and to develop a more stable and generalized system for multiclass brain MR image classification.
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.