.png){kind=icon}
返回
Unsupervised Feature Learning Classification With Radial Basis Function Extreme Learning Machine Using Graphic Processors
.png){kind=icon}
PRE
AI
DOI：10.1109/TCYB.2015.2511149.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Ever-increasing size and complexity of data sets create challenges and potential tradeoffs of accuracy and speed in learning algorithms. This paper offers progress on both fronts. It presents a mechanism to train the unsupervised learning features learned from only one layer to improve performance in both speed and accuracy. The features are learned by an unsupervised feature learning (UFL) algorithm. Then, those features are trained by a fast radial basis function (RBF) extreme learning machine (ELM). By exploiting the massive parallel computing attribute of modern graphics processing unit, a customized compute unified device architecture (CUDA) kernel is developed to further speed up the computing of the RBF kernel in the ELM. Results tested on Canadian Institute for Advanced Research and Mixed National Institute of Standards and Technology data sets confirm the UFL RBF ELM achieves high accuracy, and the CUDA implementation is up to 20 times faster than CPU and the naive parallel approach.
Keyword:
Compute unified device architecture (CUDA)
extreme learning machine (ELM)
neural network
radial basis function (RBF)
support vector machine (SVM)
期刊
IF:
10.5
论文数:
1.1W
被引数:
5.0W
机构
引用论文
Photonic Reservoir Computer with Output Expansion for Unsupervized Parameter Drift Compensation.png)
Entropy
OAAI
EntropyIF0
OAAI分享收藏Productivity enhancement of solar still by PCM and Nanoparticles miscellaneous basin absorbing materialsDesalination
OAAI
DesalinationIF0
OAAI分享收藏分享收藏
查看更多内容