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
Travel-To-School Mode Choice Modelling Employing Artificial Intelligence Techniques: A Comparative Study
TL;DR: The presented results confirm the efficacy and superiority of the ensemble method over the others and develops a majority voting ensemble method based on the outputs of the employed MLT to enhance the overall prediction performance.
Proceedings ArticleDOI
Fast Activation Function Approach for Deep Learning Based Online Anomaly Intrusion Detection
Khaled Alrawashdeh,Carla Purdy +1 more
TL;DR: This work proposes a fast activation function, namely the Adaptive Linear Function (ALF), to increase the convergence speed and accuracy of the deep leaning structure for real-time applications and outperforms state-of-the-art networks such as the Stacked Sparse AutoEncoder Based Extreme Learning Machine in both accuracy and convergence speed.
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Arbitrary Category Classification of Websites Based on Image Content
TL;DR: The methodology is robust to noise and can learn abstract target categories; website classification accuracy surpasses 97% for the most important categories considered in this study.
Proceedings ArticleDOI
Surface classification based on vibration on omni-wheel mobile base
TL;DR: This work proposes a comparison between different classifiers of identifying surfaces by using a 3-axis accelerometer sensor on a holonomic robot equipped with four omni-directional wheels and mounted on its frame to measure the whole body vibration.
Journal ArticleDOI
Visual Cue-Guided Rat Cyborg for Automatic Navigation [Research Frontier]
TL;DR: Inspired by the fact that humans usually give a series of stimuli to a rat robot, a closed-loop model is developed that issues a stimulus sequence automatically according to the state of the rat and the objects in front of it until the rat completes the motion successfully.
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.