当前位置: X-MOL 学术Mobile Netw. Appl. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Design of Extreme Learning Machine with Smoothed ℓ 0 Regularization
Mobile Networks and Applications ( IF 2.3 ) Pub Date : 2020-06-27 , DOI: 10.1007/s11036-020-01587-3
Cuili Yang , Kaizhe Nie , Junfei Qiao , Bing Li

In extreme learning machine (ELM), a large number of hidden nodes are required due to the randomly generated hidden layer. To improve network compactness, the ELM with smoothed 0 regularizer (ELM-SL0 for short) is studied in this paper. Firstly, the 0 regularization penalty term is introduced into the conventional error function, such that the unimportant output weights are gradually forced to zeros. Secondly, the batch gradient method and the smoothed 0 regularizer are combined for training and pruning ELM. Furthermore, both the weak convergence and strong convergence of ELM-SL0 are investigated. Compared with other existing ELMs, the proposed algorithm obtains better performance in terms of estimation accuracy and network sparsity.



中文翻译:

平滑ℓ0正则化的极限学习机设计

在极限学习机(ELM)中,由于随机生成的隐藏层,因此需要大量的隐藏节点。为了提高网络紧凑,具有平滑的ELM 0正则化(ELM-SL0的简称)中进行了研究。首先,0正则化惩罚项被引入到传统的误差函数,使得不重要输出权重被逐渐被迫零。其次,批次梯度法和平滑0正则化器结合在一起用于培训和修剪ELM。此外,还研究了ELM-SL0的弱收敛性和强收敛性。与其他现有的ELM相比,该算法在估计精度和网络稀疏性方面都具有更好的性能。

更新日期:2020-06-27
down
wechat
bug