Results 231 to 240 of about 607,431 (276)

In-Materio Extreme Learning Machines

2022
Nanomaterial networks have been presented as a building block for unconventional in-Materio processors. Evolution in-Materio (EiM) has previously presented a way to congure and exploit physical materials for computation, but their ability to scale as datasets get larger and more complex remains unclear.
Benedict A. H. Jones, Noura Al Moubayed, Dagou A. Zeze, Chris Groves 0001   +3 more
openaire   +2 more sources

A wavelet extreme learning machine

Neural Computing and Applications, 2015
Extreme learning machine (ELM) has been widely used in various fields to overcome the problem of low training speed of the conventional neural network. Kernel extreme learning machine (KELM) introduces the kernel method to ELM model, which is applicable in Stat ML.
Shifei Ding, Jian Zhang 0019, Xinzheng Xu, Yanan Zhang 0004   +3 more
openaire   +1 more source

An Overview of Extreme Learning Machine

2019 4th International Conference on Control, Robotics and Cybernetics (CRC), 2019
Extreme Learning Machine (ELM), as a new learning framework of Single Hidden Layer Feedforward Neural Network (SLFN), has become one of the hottest research directions in the field of artificial intelligence in recent years. It has been widely used in multiclass classification, human action recognition and other fields.
Bohua Deng, Xinman Zhang, Weiyong Gong, Dongpeng Shang   +3 more
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Stacked Extreme Learning Machines

IEEE Transactions on Cybernetics, 2015
Extreme learning machine (ELM) has recently attracted many researchers' interest due to its very fast learning speed, good generalization ability, and ease of implementation. It provides a unified solution that can be used directly to solve regression, binary, and multiclass classification problems.
Hongming Zhou, Guang-Bin Huang, Zhiping Lin 0001, Han Wang 0001, Yeng Chai Soh   +4 more
openaire   +2 more sources

Trends in extreme learning machines: A review

Neural Networks, 2015
Extreme learning machine (ELM) has gained increasing interest from various research fields recently. In this review, we aim to report the current state of the theoretical research and practical advances on this subject. We first give an overview of ELM from the theoretical perspective, including the interpolation theory, universal approximation ...
Gao Huang 0001, Guang-Bin Huang, Shiji Song, Keyou You   +3 more
openaire   +3 more sources

On the kernel Extreme Learning Machine speedup

Pattern Recognition Letters, 2015
We propose an approximate solution for the kernel Extreme Learning Machine.The proposed method reduces the computational and memory costs of kELM.The proposed approach achieves satisfactory classification performance. In this paper, we describe an approximate method for reducing the time and memory complexities of the kernel Extreme Learning Machine ...
Gabbouj Moncef, Iosifidis Alexandros, Gabbouj Moncef, Iosifidis Alexandros   +3 more
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Comments on "The Extreme Learning Machine

IEEE Transactions on Neural Networks, 2008
This comment letter points out that the essence of the ldquoextreme learning machine (ELM)rdquo recently appeared has been proposed earlier by Broomhead and Lowe and Pao , and discussed by other authors. Hence, it is not necessary to introduce a new name "ELM".
Lipo Wang, Chunru R. Wan
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BELM: Bayesian Extreme Learning Machine

IEEE Transactions on Neural Networks, 2011
The theory of extreme learning machine (ELM) has become very popular on the last few years. ELM is a new approach for learning the parameters of the hidden layers of a multilayer neural network (as the multilayer perceptron or the radial basis function neural network).
Emilio Soria-Olivas, Juan Gómez-Sanchís, José David Martín-Guerrero, Joan Vila-Francés, Marcelino Martínez-Sober, José Rafael Magdalena Benedicto, Antonio J. Serrano   +6 more
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Deep kernel learning in extreme learning machines

Pattern Analysis and Applications, 2020
Emergence of extreme learning machine as a breakneck learning algorithm has marked its prominence in solitary hidden layer feed-forward networks. Kernel-based extreme learning machine (KELM) reflected its efficiency in diverse applications where feature mapping functions of hidden nodes are concealed from users. The conventional KELM algorithms involve
Afzal A. L., Nikhitha K. Nair, S. Asharaf   +2 more
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Heterogeneous extreme learning machines

2016 International Joint Conference on Neural Networks (IJCNN), 2016
The developments in communication, sensor and computing technologies are generating information at increasing rates and the nature of the data is becoming highly heterogeneous. Accordingly, the objects under study are described by collections of variables of very different kinds (e.g. numeric, non-numeric, images, signals, videos, documents, etc.) with
openaire   +1 more source