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
Hand-drawn symbol recognition in immersive virtual reality using deep extreme learning machines
TL;DR: The recognition of symbols that are written while the user is immersed inside a room scale virtual reality experience using a consumer grade head-mounted display and related peripherals is investigated by exploring how texts and symbols can be analyzed and automatically recognized in virtual scenes of this type.
DissertationDOI
Enhanced extreme learning machines for image classification
TL;DR: In this article, the authors propose a method to solve the problem of "uniformity" and "uncertainty" in the context of health care, and propose a solution.
Book ChapterDOI
Secure Multi-Party Computation Based Privacy Preserving Extreme Learning Machine Algorithm Over Vertically Distributed Data
TL;DR: This research proposes an efficient and secure privacy-preserving learning algorithm for ELM classification over data that is vertically partitioned among several parties that preserves the privacy on numerical attributes and builds a classification model without sharing private data without disclosing the data of each party to others.
Book ChapterDOI
Spatial data reduction through element -of-interest (EOI) extraction
TL;DR: This chapter presents current state-of-the-art methods to create EOI from some types of georeferenced big data and presents use cases from both the active and passive spatial realms, including the active applications of terrain feature extraction from digital elevation models and vegetation mapping from remotely-sensed imagery.
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.