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
A mapreduce-based ELM for regression in big data
TL;DR: Through analyzing the theory of ELM, a MapReduce-Based ELM method is proposed that can efficient process big dataset on commodity hardware and it has a good performance on speedup under the cloud environment where the dataset is stored as data block in different machines.
Journal ArticleDOI
Robust Maximum Mixture Correntropy Criterion Based One-Class Classification Algorithm
TL;DR: In this article , the maximum mixture correntropy criterion (MMCC) with multiple kernels is applied to the shallow and hierarchical one-class extreme learning machine to enhance the model robustness and learning speed.
Proceedings ArticleDOI
Improving accuracy of Gaussian mixture model classifiers with additional discriminative training
Ajay M. Patrikar,John P. Baker +1 more
TL;DR: A discriminative training method based on the Moore-Penrose pseudo-inverse, often used in the ELM, is applied to the GMM classifier first trained with the EM algorithm and it is shown that on a number of benchmark pattern classification problems the proposed method improves accuracy of the GMm classifier significantly and produces results that are comparable to the SVM or ELM.
Journal ArticleDOI
From Artificial Intelligence to Cyborg Intelligence
Daniel Zeng,Zhaohui Wu +1 more
TL;DR: How has cyborg intelligence combined the best of machine and biological intelligence, and what is yet to come?
Proceedings ArticleDOI
Traffic Sign Detection using Deep Random Mapping Autoencoder Network
TL;DR: This paper proposes an effective method for traffic sign detection by employing deep random mapping autoencoder network that utilizes histogram of oriented gradient and color histogram to express the features of traffic signs.
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.