Results 261 to 270 of about 8,140,064 (302)

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 0001, Chunru 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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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
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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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Ensembling Extreme Learning Machines

2007
Extreme learning machine (ELM) is a novel learning algorithm much faster than the traditional gradient-based learning algorithms for single-hidden-layer feedforward neural networks (SLFNs). Neural network ensemble is a learning paradigm where several neural networks are jointly used to solve a problem.
Huawei Chen, Huahong Chen, Xiaoling Nian, Pei-Pei Liu   +3 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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Learning from correlation with extreme learning machine

International Journal of Machine Learning and Cybernetics, 2019
A seemingly unrelated regression (SUR) refers to several individual equations among which there is not an explicit connection such as one equation’s observation is another equation’s response, but there exists an implicit relation represented by correlated disturbances of response variables.
Li Zhao, Jie Zhu
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Genetic ensemble of extreme learning machine

Neurocomputing, 2014
Extreme learning machine (ELM) was proposed as a new learning algorithm to train single-hidden-layer feedforward neural networks (SLFNs). ELM has been proven to perform in high efficiency, however, due to the random determination of parameters for hidden nodes, some un-optimal parameters may be generated to influence the generalization performance and ...
Xiaowei Xue, Min Yao, Zhaohui Wu 0001, Jianhua Yang   +3 more
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Self-adaptive extreme learning machine

Neural Computing and Applications, 2015
In order to overcome the disadvantage of the traditional algorithm for SLFN (single-hidden layer feedforward neural network), an improved algorithm for SLFN, called extreme learning machine (ELM), is proposed by Huang et al. However, ELM is sensitive to the neuron number in hidden layer and its selection is a difficult-to-solve problem.
Gaige Wang, Mei Lu, Yong-Quan Dong, Xiang-Jun Zhao   +3 more
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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 0011, Eric C. C. Tsang, Xi-Zhao Wang, Rana Aamir Raza Ashfaq   +3 more
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