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
Haptic recognition using hierarchical extreme learning machine with local-receptive-field
TL;DR: This paper proposes an effective hierarchical extreme learning machine with local-receptive-field architecture, while introducing the local receptive field concept in neuroscience and maintaining ELM’s advantages of training efficiency.
Journal ArticleDOI
Urban Traffic Congestion Evaluation Based on Kernel the Semi-Supervised Extreme Learning Machine
Qing Shen,Xiaojuan Ban,Chong Guo +2 more
TL;DR: This method, semi-supervised learning can integrate both small-scale labeled data and large-scale unlabeled data, so that they can play their respective advantages, while the ELM can process large scale data at high speed.
Journal ArticleDOI
Ensemble RBM-based classifier using fuzzy integral for big data classification
TL;DR: Experiments show that the proposed ensemble approach for big data classification based on Hadoop MapReduce and fuzzy integral can outperform other baseline methods to achieve state-of-the-art performance.
Proceedings ArticleDOI
Combining Modality-Specific Extreme Learning Machines for Emotion Recognition in the Wild
Heysem Kaya,Albert Ali Salah +1 more
TL;DR: The proposed system utilizes Extreme Learning Machines (ELM) for modeling modality-specific features and combines the scores for final prediction and the best results for both modalities are obtained with Kernel ELM compared to basic ELM.
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.