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Support vector machine regression for the prediction of maize hybrid performance

Steven Maenhout (UGent) , Bernard De Baets (UGent) , Geert Haesaert (UGent) and Erik Van Bockstaele (UGent)
(2007) THEORETICAL AND APPLIED GENETICS. 115(7). p.1003-1013
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MLA
Maenhout, Steven, Bernard De Baets, Geert Haesaert, et al. “Support Vector Machine Regression for the Prediction of Maize Hybrid Performance.” THEORETICAL AND APPLIED GENETICS 115.7 (2007): 1003–1013. Print.
APA
Maenhout, S., De Baets, B., Haesaert, G., & Van Bockstaele, E. (2007). Support vector machine regression for the prediction of maize hybrid performance. THEORETICAL AND APPLIED GENETICS, 115(7), 1003–1013.
Chicago author-date
Maenhout, Steven, Bernard De Baets, Geert Haesaert, and Erik Van Bockstaele. 2007. “Support Vector Machine Regression for the Prediction of Maize Hybrid Performance.” Theoretical and Applied Genetics 115 (7): 1003–1013.
Chicago author-date (all authors)
Maenhout, Steven, Bernard De Baets, Geert Haesaert, and Erik Van Bockstaele. 2007. “Support Vector Machine Regression for the Prediction of Maize Hybrid Performance.” Theoretical and Applied Genetics 115 (7): 1003–1013.
Vancouver
1.
Maenhout S, De Baets B, Haesaert G, Van Bockstaele E. Support vector machine regression for the prediction of maize hybrid performance. THEORETICAL AND APPLIED GENETICS. Springer; 2007;115(7):1003–13.
IEEE
[1]
S. Maenhout, B. De Baets, G. Haesaert, and E. Van Bockstaele, “Support vector machine regression for the prediction of maize hybrid performance,” THEORETICAL AND APPLIED GENETICS, vol. 115, no. 7, pp. 1003–1013, 2007.
@article{414329,
  author       = {Maenhout, Steven and De Baets, Bernard and Haesaert, Geert and Van Bockstaele, Erik},
  issn         = {0040-5752},
  journal      = {THEORETICAL AND APPLIED GENETICS},
  language     = {eng},
  number       = {7},
  pages        = {1003--1013},
  publisher    = {Springer},
  title        = {Support vector machine regression for the prediction of maize hybrid performance},
  volume       = {115},
  year         = {2007},
}

Web of Science
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