Results 221 to 230 of about 607,431 (276)

Evolutionary extreme learning machine

Pattern Recognition, 2005
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
A K Qin, Ponnuthurai Nagaratnam Suganthan, Guang-Bin Huang   +2 more
exaly   +2 more sources

Extreme ensemble of extreme learning machines

Statistical Analysis and Data Mining: The ASA Data Science Journal, 2020
AbstractExtreme learning machine (ELM) has attracted attentions in pattern classification problems due to its preferences in low computations and high generalization. To overcome its drawbacks, caused by the randomness of input weights and biases, the ensemble of ELMs was proposed.
Eghbal G. Mansoori, Massar Sara
openaire   +1 more source

Learning to Rank with Extreme Learning Machine

Neural Processing Letters, 2013
Relevance ranking has been a popular and interesting topic over the years, which has a large variety of applications. A number of machine learning techniques were successfully applied as the learning algorithms for relevance ranking, including neural network, regularized least square, support vector machine and so on.
Weiwei Zong, Guang-Bin Huang
openaire   +1 more source

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 ...
Guang-Bin Huang, Dianhui Wang 0001, Yuan Lan   +2 more
openaire   +1 more source

Robust extreme learning machine

Neurocomputing, 2013
The output weights computing of extreme learning machine (ELM) encounters two problems, the computational and outlier robustness problems. The computational problem occurs when the hidden layer output matrix is a not full column rank matrix or an ill-conditioned matrix because of randomly generated input weights and biases. An existing solution to this
Punyaphol Horata, Sirapat Chiewchanwattana, Khamron Sunat   +2 more
openaire   +1 more source

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 0001, Zhihua Cai, Peng Zhang 0001, Ling Chen 0006   +4 more
openaire   +1 more source

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 0017, Chuanhou Gao
openaire   +1 more source

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 0005
openaire   +1 more source

Weighted extreme learning machine for imbalance learning

Neurocomputing, 2013
Extreme learning machine (ELM) is a competitive machine learning technique, which is simple in theory and fast in implementation. The network types are ''generalized'' single hidden layer feedforward networks, which are quite diversified in the form of variety in feature mapping functions or kernels. To deal with data with imbalanced class distribution,
Weiwei Zong, Guang-Bin Huang, Yiqiang Chen   +2 more
exaly   +3 more sources

Ordinal extreme learning machine

Neurocomputing, 2010
Recently, a new fast learning algorithm called Extreme Learning Machine (ELM) has been developed for Single-Hidden Layer Feedforward Networks (SLFNs) in G.-B. Huang, Q.-Y. Zhu and C.-K. Siew ''[Extreme learning machine: theory and applications,'' Neurocomputing 70 (2006) 489-501].
Wanyu Deng, Qinghua Zheng, Shiguo Lian, Lin Chen 0005, Xin Wang   +4 more
openaire   +1 more source