Results 281 to 290 of about 606,530 (324)

Extreme Learning Machines

2018
<p>Extreme Learning Machine (ELM) is a recently discovered way of training Single Layer Feed-forward Neural Networks with an explicitly given solution, which exists because the input weights and biases are generated randomly and never change. The method in general achieves performance comparable to Error Back-Propagation, but the training time is
Anton Akusok, Amaury Lendasse, Stephen Baek, Andrew Kusiak, Gregory R. Carmichael, Yong Chen, Ching-Long Lin   +6 more
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Regularized Extreme Learning Machine

2009 IEEE Symposium on Computational Intelligence and Data Mining, 2009
Extreme Learning Machine proposed by Huang G-B has attracted many attentions for its extremely fast training speed and good generalization performance. But it still can be considered as empirical risk minimization theme and tends to generate over-fitting model.
Wanyu Deng, Qinghua Zheng, Lin Chen
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Memetic Extreme Learning Machine

Pattern Recognition, 2016
Extreme Learning Machine (ELM) is a promising model for training single-hidden layer feedforward networks (SLFNs) and has been widely used for classification. However, ELM faces the challenge of arbitrarily selected parameters, e.g., the network weights and hidden biases. Therefore, many efforts have been made to enhance the performance of ELM, such as
Yongshan Zhang, Jia Wu, Zhihua Cai, Peng Zhang, Ling Chen   +4 more
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Symmetric extreme learning machine

Neural Computing and Applications, 2012
Extreme learning machine (ELM) can be considered as a black-box modeling approach that seeks a model representation extracted from the training data. In this paper, a modified ELM algorithm, called symmetric ELM (S-ELM), is proposed by incorporating a priori information of symmetry.
Xueyi Liu, Ping Li, Chuanhou Gao
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Multilayer one-class extreme learning machine

Neural Networks, 2019
One-class classification has been found attractive in many applications for its effectiveness in anomaly or outlier detection. Representative one-class classification algorithms include the one-class support vector machine (SVM), Naive Parzen density estimation, autoencoder (AE), etc.
Haozhen, Dai, Jiuwen, Cao, Tianlei, Wang, Muqing, Deng, Zhixin, Yang   +4 more
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Quaternion Extreme Learning Machine

2017
Quaternion signal processing has been an increasing popular research topic for its application in a wide range of fields, and extreme learning machine (ELM) is an emerging training strategy for the generalized single hidden layer feedforward neural networks.
Hui Lv, Huisheng Zhang
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Dimension Reduction With Extreme Learning Machine

IEEE Transactions on Image Processing, 2016
Data may often contain noise or irrelevant information, which negatively affect the generalization capability of machine learning algorithms. The objective of dimension reduction algorithms, such as principal component analysis (PCA), non-negative matrix factorization (NMF), random projection (RP), and auto-encoder (AE), is to reduce the noise or ...
Liyanaarachchi Lekamalage Chamara Kasun, Yan Yang, Guang-Bin Huang, Zhengyou Zhang   +3 more
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Extreme Learning Machine for Multilayer Perceptron

IEEE Transactions on Neural Networks and Learning Systems, 2016
Extreme learning machine (ELM) is an emerging learning algorithm for the generalized single hidden layer feedforward neural networks, of which the hidden node parameters are randomly generated and the output weights are analytically computed. However, due to its shallow architecture, feature learning using ELM may not be effective for natural signals ...
Jiexiong, Tang, Chenwei, Deng, Guang-Bin, Huang   +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
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Timeliness online regularized extreme learning machine

International Journal of Machine Learning and Cybernetics, 2016
A novel online sequential extreme learning machine (ELM) algorithm with regularization mechanism in a unified framework is proposed in this paper. This algorithm is called timeliness online regularized extreme learning machine (TORELM). Like the timeliness managing extreme learning machine (TMELM) which incorporates timeliness management scheme into ...
Xiong Luo, Xiaona Yang, Changwei Jiang, Xiaojuan Ban   +3 more
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