Non-negative Matrix Factorization Based Learning from Label Proportions for Vehicle Loan Default Detection
Publication
, Conference
Li, H; Tong, Q; Wang, B
Published in: Procedia Computer Science
2019
Duke Scholars
Published In
Procedia Computer Science
DOI
ISSN
1877-0509
Publication Date
2019
Volume
162
Start / End Page
878 / 886
Publisher
Elsevier BV
Related Subject Headings
- 46 Information and computing sciences
- 10 Technology
- 08 Information and Computing Sciences
Citation
APA
Chicago
ICMJE
MLA
NLM
Li, H., Tong, Q., & Wang, B. (2019). Non-negative Matrix Factorization Based Learning from Label Proportions for Vehicle Loan Default Detection. In Procedia Computer Science (Vol. 162, pp. 878–886). Elsevier BV. https://doi.org/10.1016/j.procs.2019.12.063
Li, Hai, Qiang Tong, and Bo Wang. “Non-negative Matrix Factorization Based Learning from Label Proportions for Vehicle Loan Default Detection.” In Procedia Computer Science, 162:878–86. Elsevier BV, 2019. https://doi.org/10.1016/j.procs.2019.12.063.
Li H, Tong Q, Wang B. Non-negative Matrix Factorization Based Learning from Label Proportions for Vehicle Loan Default Detection. In: Procedia Computer Science. Elsevier BV; 2019. p. 878–86.
Li, Hai, et al. “Non-negative Matrix Factorization Based Learning from Label Proportions for Vehicle Loan Default Detection.” Procedia Computer Science, vol. 162, Elsevier BV, 2019, pp. 878–86. Crossref, doi:10.1016/j.procs.2019.12.063.
Li H, Tong Q, Wang B. Non-negative Matrix Factorization Based Learning from Label Proportions for Vehicle Loan Default Detection. Procedia Computer Science. Elsevier BV; 2019. p. 878–886.
Published In
Procedia Computer Science
DOI
ISSN
1877-0509
Publication Date
2019
Volume
162
Start / End Page
878 / 886
Publisher
Elsevier BV
Related Subject Headings
- 46 Information and computing sciences
- 10 Technology
- 08 Information and Computing Sciences