Predicting body and carcass characteristics of 2 broiler chicken strains using support vector regression and neural network models

Poultry Science

Volumn 91, Issue 12, 2012, Pages 3286-3294

  Faridi, A ^a   Sakomura, N K ^b   Golian, A ^a   Marcato, S M ^c  

^a FERDOWSI UNIVERSITY OF MASHHAD   (Iran)

^b SÃO PAULO STATE UNIVERSITY UNESP   (Brazil)

^c STATE UNIVERSITY OF MARINGÁ   (Brazil)

Author keywords

Carcass characteristics; Neural network; Support vector regression

Indexed keywords

EID: 84869751787     PISSN: 00325791     EISSN: 15253171     Source Type: Journal    
DOI: 10.3382/ps.2012-02491     Document Type: Article

References (21)

1. Burges, C. J. C. 1998. A tutorial on support vector machines for pattern recognition. Data Min. Knowl. Discov. 2:121-167.
2. Cheng, C. S., P. W. Chen, and K. K. Huang. 2011. Estimating the shift size in the process mean with support vector regression and neural networks. Exp. Sys. Applic. 38:10624-10630.
3. Cristianini, N., and J. Shawe-Taylor. 2000. Introduction to support vector machines and other kernel-based learning methods. Cambridge University Press, Cambridge, UK.
4. Dreiseitl, S., and L. Ohno-Machado. 2002. Logistic regression and artificial neural network classification models: A methodology review. J. Biomed. Inform. 35:352-359.
5. Faridi, A., and A. Golian. 2011. Use of neural network models to estimate early egg production in broiler breeder through dietary nutrient intake. Poult. Sci. 90:2897-2903.
6. Faridi, A., M. Mottaghitalab, H. Darmani-kuhi, J. France, and H. Ahmadi. 2011. Predicting carcass energy content and composition in broilers using the group method of data handling-type neural networks. J. Agric. Sci. Camb. 149:249-254.
7. Javed, S. G., A. Khan, A. Majid, A. M. Majid, and J. Bashir. 2007. Lattice constant prediction of orthorhombic ABO3 perovskites using support vector machines. Comput. Mater. Sci. 39:627-634.
8. Kalinowski, A., E. T. Moran Jr., and C. L. Wyatt. 2003. Methio-nine and cystine requirements of slow- and fast-feathering broiler males from three to six weeks of age. Poult. Sci. 82:1428-1437.
9. Kamran, Z., M. Sarwar, M. Nisa, M. A. Nadeem, S. Mahmood, M. E. Babar, and S. Ahmed. 2008. Effect of low-protein diets hav-ing constant energy-to-protein ratio on performance and carcass characteristics of broiler chickens from one to thirty-five days of age. Poult. Sci. 87:468-474.
10. Kim, K. 2003. Financial time series forecasting using support vector machines. Neurocomputing 55:307-319.
11. Marcato, S. M., N. K. Sakomura, D. P. Munari, J. B. K. Fernandes, I. M. Kawauchi, and M. A. Bonato. 2008. Growth and body nutrient deposition of two broiler commercial genetic lines. Braz. J. Poult. Sci. 10:117-123.
12. Martens, D., L. Bruynseels, B. Baesens, M. Willekens, and J. Van-thienen. 2008. Predicting going concern opinion with data mining. Deci. Supp. Sys. 45:765-777.
13. Mountrakis, G., J. Im, and C. Ogole. 2011. Support vector machines in remote sensing: A review. J. Photo. Remo. Sen. 66:247-259.
14. Nandi, S., Y. Badhe, J. Lonari, U. Sridevi, B. S. Rao, S. S. Tambe, and B. D. Kulkarni. 2004. Hybrid process modeling and optimization strategies integrating neural networks/support vector regression and genetic algorithms: Study of benzene isopropylation on Hbeta catalyst. Chem. Eng. J. 97:115-129.
15. Sakomura, N. K., R. M. Gous, S. M. Marcato, and J. B. K. Fer-nandes. 2011. A description of the growth of the major body components of 2 broiler chicken strains. Poult. Sci. 90:2888-2896.
16. StatSoft. 2009. Statistica Data Analysis Software System. Version 7.1. StatSoft Inc., Tulsa, OK.
17. Tay, E. H., and L. Cao. 2001. Application of support vector machines in financial time series forecasting. Int. J. Manag. Sci. 29:309-317.
18. Vapnik, V. N. 1995. The Nature of Statistical Learning Theory. Springer, New York, NY.
19. Vapnik, V. N., S. Golowich, and A. Smola. 1997. Support vector method for function approximation, regression estimation and signal processing. Vol. 9, pages 281-287 in Advance in Neural Information Processing System. M. Mozer, M. Jordan, and T. Petsche, ed. MIT Press, Cambridge, MA.
20. Yang, C. C., and M. D. Shieh. 2010. A support vector regression based prediction model of affective responses for product form design. Comput. Ind. Eng. 59:682-689.
21. Zhao, J. P., J. L. Chen, G. P. Zhao, M. Q. Zheng, R. R. Jiang, and J. Wen. 2009. Live performance, carcass composition, and blood metabolite responses to dietary nutrient density in two distinct broiler breeds of male chickens. Poult. Sci. 88:2575-2584.