期刊文献+
共找到1篇文章
< 1 >
每页显示 20 50 100
A Double-Weighted Deterministic Extreme Learning Machine Based on Sparse Denoising Autoencoder and Its Applications
1
作者 Liang Luo Bolin Liao +1 位作者 Cheng Hua Rongbo Lu 《Journal of Computer and Communications》 2022年第11期138-153,共16页
Extreme learning machine (ELM) is a feedforward neural network-based machine learning method that has the benefits of short training times, strong generalization capabilities, and will not fall into local minima. Howe... Extreme learning machine (ELM) is a feedforward neural network-based machine learning method that has the benefits of short training times, strong generalization capabilities, and will not fall into local minima. However, due to the traditional ELM shallow architecture, it requires a large number of hidden nodes when dealing with high-dimensional data sets to ensure its classification performance. The other aspect, it is easy to degrade the classification performance in the face of noise interference from noisy data. To improve the above problem, this paper proposes a double pseudo-inverse extreme learning machine (DPELM) based on Sparse Denoising AutoEncoder (SDAE) namely, SDAE-DPELM. The algorithm can directly determine the input weight and output weight of the network by using the pseudo-inverse method. As a result, the algorithm only requires a few hidden layer nodes to produce superior classification results when classifying data. And its combination with SDAE can effectively improve the classification performance and noise resistance. Extensive numerical experiments show that the algorithm has high classification accuracy and good robustness when dealing with high-dimensional noisy data and high-dimensional noiseless data. Furthermore, applying such an algorithm to Miao character recognition substantiates its excellent performance, which further illustrates the practicability of the algorithm. 展开更多
关键词 Extreme Learning Machine sparse denoising autoencoder Pseudo-Inverse Method Miao Character Recognition
下载PDF
上一页 1 下一页 到第
使用帮助 返回顶部