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
Reduced Kernel Extreme Learning Machine for Traffic Sign Recognition
TL;DR: This paper proposes a TSR system based on a Reduced Kernel Extreme Learning machine (RK-ELM) which is efficiently implemented in a Graphic Processing Unit (GPU) because of the inherent simplicity of ELM-based models.
Book ChapterDOI
Fast Handwritten Digit Recognition with Multilayer Ensemble Extreme Learning Machine
TL;DR: A novel classifier based on Extreme Learning Machine is proposed that achieves competitive accuracy results while keeping training times low and is called multilayer ensemble Extreme learning Machine.
Modeling conventional and hyperspectral image-sets for classification
TL;DR: This thesis proposes efficient and accurate representations to model conventional and hyperspectral image-sets and performs a detailed study on whether the spectral reflectance of the face alone can be used as a reliable biometric.
Journal ArticleDOI
Parallel methods for linear systems solution in extreme learning machines: an overview
TL;DR: An updated review of parallel algorithms for solving square and rectangular single and double precision matrix linear systems using multi-core central processing units and graphic processing units based on the review of papers reported in the literature during the last five years is presented.
Book ChapterDOI
On Mutual Information over Non-Euclidean Spaces, Data Mining and Data Privacy Levels
TL;DR: This paper proposes to use Mutual Information over non-Euclidean spaces as a means of measuring the impact of data privacy techniques, in information theoretic and in data mining terms.
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.