Results 241 to 250 of about 601,272 (282)

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
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, Chuanhou Gao
openaire   +1 more source

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
openaire   +2 more sources

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
openaire   +1 more source

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
openaire   +2 more sources

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
openaire   +2 more sources

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

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
openaire   +1 more source

Discriminative clustering via extreme learning machine

Neural Networks, 2015
Discriminative clustering is an unsupervised learning framework which introduces the discriminative learning rule of supervised classification into clustering. The underlying assumption is that a good partition (clustering) of the data should yield high discrimination, namely, the partitioned data can be easily classified by some classification ...
Huang, Gao, Liu, Tianchi, Yang, Yan, Lin, Zhiping, Song, Shiji, Wu, Cheng   +5 more
openaire   +2 more sources

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, Peipei Liu   +3 more
openaire   +1 more source