Active learning Bayesian support vector regression model for global approximation - Citegraph

Paper Info

Title
Active learning Bayesian support vector regression model for global approximation

Abstract
•Two new SVR models are developed through Bayesian inference technique.•The presented SVR models provide prediction error bar and confidence interval.•An active learning algorithm is presented to refine the SVR model adaptively.•Six benchmark functions are used to validate the performance of the method.

Year	DOI	Venue
2021	10.1016/j.ins.2020.08.090	Information Sciences
Keywords	DocType	Volume
Support vector regression,Bayesian inference,Active learning,Supervised learning	Journal	544
ISSN	Citations	PageRank
0020-0255	0	0.34
References	Authors
0	2

Authors (2 rows)

Cited by (0 rows)

References (0 rows)

Name	Order	Citations	PageRank
Kai Cheng	1	39	12.36
Zhenzhou Lu	2	182	33.11

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