.png){kind=icon}
返回
Broad learning system based ensemble deep model
.png){kind=icon}
PRE
AI
DOI：10.1007/s00500-022-07004-z.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Broad learning system (BLS) demonstrates a novel structure of neural networks based on random vector functional link network (RVFL), which has a faster modeling speed, better generalization ability, higher regression accuracy for solving regression tasks. To improve the feature representation capacity of BLS and guarantee the training efficiency, a novel ensemble deep model based on broad learning system is proposed, named as EDBLS. Unlike other deep or ensemble models of BLS, the results of EDBLS can be calculated by training the model only once. Meanwhile, the performance of BLS mainly depends on its structure, it is the same as EDBLS, we need to select network parameters for different real-world problems. As the lower efficiency of grid search, it is no longer suitable for EDBLS with more parameters. Therefore, coot optimization algorithm (COOT) is applied to determine the network structure of EDBLS for regression problems, it is important to note that the COOT is only a tool for determining the structure of EDBLS. Finally, the experimental results on MNIST, NORB and some regression benchmark datasets show that EDBLS has a better classification and regression performance, COOT is beneficial to select the network parameters of EDBLS for regression.
Keyword:
Broad learning system
Ensemble deep learning
Coot optimization algorithm
Neural network
Optimization
期刊
IF:
2.5
论文数:
1.0W
被引数:
2.1W
机构
暂无机构信息
引用论文
分享收藏A support vector regression model hybridized with chaotic krill herd algorithm and empirical mode decomposition for regression task基于混沌磷虾群算法和经验模式分解的支持向量回归模型的回归任务.png)
NEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
分享收藏Adaptive Deep Cascade Broad Learning System and Its Application in Image Denoising自适应深度级联广义学习系统及其在图像去噪中的应用
PREAI
分享收藏分享收藏LPI-BLS: Predicting lncRNA-protein interactions with a broad learning system-based stacked ensemble classifierLpi-bls: 使用基于广泛学习系统的堆叠集成分类器预测lncRNA-蛋白质相互作用NEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
分享收藏
查看更多内容