摘要
作为一种前馈神经网络,宽度学习系统因其精度高、训练速度快且能有效代替深度学习方法而备受研究者的关注。然而,宽度学习系统存在对网络中的特征节点个数比较敏感且求伪逆方式易使模型出现过拟合等问题。为此,在宽度学习系统中引入贝叶斯推断和图正则化。一方面,通过引入先验知识进行贝叶斯学习可以有效提高权重的稀疏性,提高模型的稳定性;另一方面,加入图正则化可充分考虑数据内在的图信息,进一步提高模型的泛化能力。在UCI数据集和NORB数据集上对所提模型进行性能评估,实验结果表明,所提的基于图正则化的贝叶斯宽度学习系统模型能进一步提高宽度学习系统的分类精度且具有更好的稳定性。
As a feed forward neural network,broad learning system(BLS)has attracted much attention because of its high accuracy,fast training speed,and the ability to effectively replace deep learning methods.However,it is sensitive to the number of feature nodes and the pseudo-inverse method is likely to result in the problem of over fitting for BLS mod-el.To address the above issues,Bayesian inference and graph regularization was introduced in to the BLS model.By in-troducing the prior knowledge for Bayesian learning,the sparsity of the weights and the stability of the model could be effectively improved;while the graph information mining from the data could be fully considered to improve the genera-lization ability of the model by regularization.The UCI and NORB dataset were adopted for evaluating the performance of the proposed model.The experiment results demonstrated that the proposed graph-regularized Bayesian broad learning system model can further improve the accuracy of classification and has better stability.
作者
段俊伟
许林灿
全渝娟
陈龙
陈俊龙
DUAN Junwei;XU Lincan;QUAN Yujuan;CHEN Long;CHEN C.L.Philip(College of Information Science and Technology,Jinan University,Guangzhou 510632,China;Faculty of Science and Technology,University of Macao,Macao 999078,China;School of Computer Science and Engineering,South China University of Technology,Guangzhou 510006,China)
出处
《智能科学与技术学报》
2022年第1期109-117,共9页
Chinese Journal of Intelligent Science and Technology
基金
国家重点研发计划基金资助项目(No.2018YFC2002500)
广东省基础与应用基础研究基金资助项目(No.2021A1515011999)
广州市科技创新发展专项资金项目(No.201902010041)。
关键词
宽度学习系统
贝叶斯推断
图正则化
模式识别
board learning system
Bayesian inference
graph regularization
pattern recognition
