Results 251 to 260 of about 601,272 (282)

Graph Convolutional Extreme Learning Machine

2020 International Joint Conference on Neural Networks (IJCNN), 2020
Extreme Learning Machine (ELM) has gained lots of research interest due to its universal approximation capability and fast learning speed. However, traditional ELMs are devised for regular Euclidean data, such as 2D grid and 1D sequence, and thus don’t apply to non-Euclidean data, e.g., graph-structured data.
Zijia Zhang, Yaoming Cai, Wenyin Gong, Xiaobo Liu, Zhihua Cai   +4 more
openaire   +1 more source

Data Partition Learning With Multiple Extreme Learning Machines

IEEE Transactions on Cybernetics, 2015
As demonstrated earlier, the learning accuracy of the single-layer-feedforward-network (SLFN) is generally far lower than expected, which has been a major bottleneck for many applications. In fact, for some large real problems, it is accepted that after tremendous learning time (within finite epochs), the network output error of SLFN will stop or ...
Yimin, Yang, Q M J, Wu, Yaonan, Wang, K M, Zeeshan, Xiaofeng, Lin, Xiaofang, Yuan   +5 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, Guang-Bin Huang, Shiji Song, Keyou You   +3 more
openaire   +3 more sources

OP-ELM: Optimally Pruned Extreme Learning Machine

IEEE Transactions on Neural Networks, 2010
In this brief, the optimally pruned extreme learning machine (OP-ELM) methodology is presented. It is based on the original extreme learning machine (ELM) algorithm with additional steps to make it more robust and generic. The whole methodology is presented in detail and then applied to several regression and classification problems.
Sorjamaa, Antti, Bas, Patrick, Simula, Olli, Jutten, Christian, Lendasse, Amaury, Miche, Yoan   +6 more
openaire   +4 more sources

Online Sequential Extreme Learning Machine With Kernels

IEEE Transactions on Neural Networks and Learning Systems, 2015
The extreme learning machine (ELM) was recently proposed as a unifying framework for different families of learning algorithms. The classical ELM model consists of a linear combination of a fixed number of nonlinear expansions of the input vector. Learning in ELM is hence equivalent to finding the optimal weights that minimize the error on a dataset ...
SCARDAPANE, SIMONE, COMMINIELLO, DANILO, SCARPINITI, MICHELE, UNCINI, Aurelio   +3 more
openaire   +3 more sources

Extreme learning machines: a survey

International Journal of Machine Learning and Cybernetics, 2011
Computational intelligence techniques have been used in wide applications. Out of numerous computational intelligence techniques, neural networks and support vector machines (SVMs) have been playing the dominant roles. However, it is known that both neural networks and SVMs face some challenging issues such as: (1) slow learning speed, (2) trivial ...
Huang, Guang-Bin., Wang, Dianhui., Lan, Yuan.   +2 more
openaire   +1 more source

Robust incremental extreme learning machine

2014 13th International Conference on Control Automation Robotics & Vision (ICARCV), 2014
Extreme Learning Machine (ELM) is a special single-hidden-layer feedforward neural networks with very fast learning speed and has attracted significant research attentions in recent years. The salient feature of ELM is that the input parameters can be randomly generated instead of being exhaustively tuned, and thus saving a great deal of computational ...
Zhifei Shao, Meng Joo Er, Ning Wang
openaire   +1 more source

Monotonic classification extreme learning machine

Neurocomputing, 2017
Monotonic classification problems mean that both feature values and class labels are ordered and monotonicity relationships exist between some features and the decision label. Extreme Learning Machine (ELM) is a single-hidden layer feedforward neural network with fast training rate and good generalization capability, but due to the existence of ...
Hong Zhu, Eric C.C. Tsang, Xi-Zhao Wang, Rana Aamir Raza Ashfaq   +3 more
openaire   +1 more source

Microbial diversity in extreme environments

Nature Reviews Microbiology, 2021
Wen-Sheng Shu, Li-Nan Huang
exaly  

Ultra-high temperature ceramics for extreme environments

Nature Reviews Materials, 2023
Elizabeth
exaly