Journal ArticleDOI
Trends in extreme learning machines
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In this paper, the authors report the current state of the theoretical research and practical advances on this subject and provide a comprehensive view of these advances in ELM together with its future perspectives.About:
This article is published in Neural Networks.The article was published on 2015-01-01. It has received 1289 citations till now. The article focuses on the topics: Extreme learning machine & Stability (learning theory).read more
Citations
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Advanced Spectral Classifiers for Hyperspectral Images: A review
TL;DR: The classification of hyperspectral images is a challenging task for a number of reasons, such as the presence of redundant features, the imbalance among the limited number of available training samples, and the high dimensionality of the data.
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Comprehensive survey of deep learning in remote sensing: theories, tools, and challenges for the community
TL;DR: In this article, the authors provide a comprehensive survey of state-of-the-art remote sensing deep learning research for remote sensing applications, focusing on theories, tools, and challenges for the remote sensing community.
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An enhanced extreme learning machine model for river flow forecasting: State-of-the-art, practical applications in water resource engineering area and future research direction
TL;DR: The obtained results reveal that the EELM model is a robust expert model and can be embraced practically in real-life water resources management and river sustainability decisions.
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Fusing audio, visual and textual clues for sentiment analysis from multimodal content
TL;DR: This paper proposes a novel methodology for multimodal sentiment analysis, which consists in harvesting sentiments from Web videos by demonstrating a model that uses audio, visual and textual modalities as sources of information.
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A review on neural networks with random weights
TL;DR: This paper objectively reviews the advantages and disadvantages of N NRW model, tries to reveal the essence of NNRW, and provides some useful guidelines for users to choose a mechanism to train a feed-forward neural network.
References
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Book
The Nature of Statistical Learning Theory
TL;DR: Setting of the learning problem consistency of learning processes bounds on the rate of convergence ofLearning processes controlling the generalization ability of learning process constructing learning algorithms what is important in learning theory?
Journal ArticleDOI
Support-Vector Networks
Corinna Cortes,Vladimir Vapnik +1 more
TL;DR: High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated and the performance of the support- vector network is compared to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.
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Learning representations by back-propagating errors
TL;DR: Back-propagation repeatedly adjusts the weights of the connections in the network so as to minimize a measure of the difference between the actual output vector of the net and the desired output vector, which helps to represent important features of the task domain.
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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
Least Squares Support Vector Machine Classifiers
TL;DR: A least squares version for support vector machine (SVM) classifiers that follows from solving a set of linear equations, instead of quadratic programming for classical SVM's.