.png){kind=icon}
返回
Double-kernel based Bayesian approximation broad learning system with dropout☆ ☆
.png){kind=icon}
PRE
AI
DOI：10.1016/j.neucom.2024.128533.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Broad learning system (BLS) is an efficient incremental learning machine algorithm. However, there are some disadvantages in such an algorithm. For example, the number of hidden layer nodes needs to be manually adjusted during the training process, meanwhile the large uncertainty will be caused by two random mappings. To solve these problems, based on the optimization ability of the kernel function, a double-kernel broad learning system (DKBLS) is proposed to eliminate the uncertainty of random mapping by using additive kernel strategy. Meanwhile, to reduce the computing costs and training time of DKBLS, a double-kernel based bayesian approximation broad learning system with dropout (Dropout-DKBLS) is further proposed. Ablation experiments show that the output accuracy of Dropout-DKBLS does not decrease even if the node is dropped. In addition, function approximation experiments show that DKBLS and Dropout-DKBLS have good robustness and can accurately predict noise data. The regression and classification experiments on multiple datasets are compared with the latest kernel-based learning methods. The comparison results show that both DKBLS and Dropout-DKBLS have good regression and classification performance. By further comparing the training time of these kernel-based learning methods, we prove that the Dropout-DKBLS can reduce the computational cost while ensuring the output accuracy.
Keyword:
Broad learning system
Double-kernel
Dropout
Bayesian approximation
期刊
IF:
6.5
论文数:
2.5W
被引数:
7.2W
机构
引用论文
Robust manifold broad learning system for large-scale noisy chaotic time series prediction: A perturbation perspective.png)
NEURAL NETWORKS
NEURAL NETWORKSIF6.3
PREAI
分享收藏
An improved Elman neural network with piecewise weighted gradient for time series predictionNEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
分享收藏Active preference-based Gaussian process regression for reward learning and optimization基于主动偏好的高斯过程回归用于奖励学习和优化
PREAI
分享收藏Blue Sky Protection Campaign: Assessing the Role of Digital Technology in Reducing Air PollutionSystems
OAAI
SystemsIF0
OAAI分享收藏分享收藏
查看更多内容