Multitask Sparse Bayesian Machine Learning with Applications in Modeling of Seismic Attenuation and Clay Parameters with Small Data Sets

dc.contributor.author	Gao, Jingze
dc.contributor.author	Wei, Shiyin
dc.contributor.author	Huang, Yong
dc.date.accessioned	2026-08-20T21:25:52Z
dc.date.available	2026-08-20T21:25:52Z
dc.date.copyright	2025/09/19
dc.date.issued	2026
dc.identifier.other	JCCEE5.CPENG-6822.pdf
dc.identifier.uri	http://yetl.yabesh.ir/yetl1/handle/yetl/4314440
dc.description.abstract	AbstractMultitask sparse Bayesian learning (SBL) has received extensive attention because it makes full use of multiple groups of data by joint learning of sparse representations for multiple models. It is capable of producing superior performance, ...
dc.publisher	American Society of Civil Engineers
dc.title	Multitask Sparse Bayesian Machine Learning with Applications in Modeling of Seismic Attenuation and Clay Parameters with Small Data Sets
dc.type	Journal Article
dc.journal.volume	40
dc.journal.issue	1
dc.journal.title	Journal of Computing in Civil Engineering
dc.identifier.doi	10.1061/JCCEE5.CPENG-6822
dc.journal.fristpage	04025109-1
dc.journal.lastpage	04025109-19
dc.page	19
dc.tree	Journal of Computing in Civil Engineering:;2026:;Volume ( 040 ):;issue: 001
dc.contenttype	Fulltext