caa a22 4500 47707300X CHVBK 20180405111431.0 cr unu---uuuuu 170330e19961201xx s 000 0 eng 10.1007/BF01413822 doi (NATIONALLICENCE)springer-10.1007/BF01413822 Neural networks applied to a large biological database to analyse dairy breeding patterns [Elektronische Daten] [G. Finn, R. Lister, T. Szabo, D. Simonetta, H. Mulder, R. Young] The paper recounts the investigation of a dairy sire prediction capability based on Cascade Correlation neural networks to study influences relating the performance of offspring to their parents. The context of the problem is the artificial insemination breeding program for the Australian dairy industry. The networks are used to screen observed information in the database to relate it to best combinations of dam and sire. The voluminous data is quite noisy and is subject to genetic and environmental influences. The intention is to extract linear and nonlinear relationships from among the input variables without specifying their form. A number of scenarios are employed which recast the data into different forms. In particular, it was discovered that the problem could be restructured and the data supplemented with transformed data to produce succinct input patterns of manageable dimensionality, which allowed for a substantially improved predictive capability. It was then found that reasonable daughter predictions could be obtained of about 10%, as measured by her milk production. Results are compared with those obtained using two alternate neural network methods. Crude statistical methods are employed to evaluate the performance of the neural networks. Springer-Verlag London Limited, 1996 Cascade correlation nationallicence Dairy breeding nationallicence Neural networks nationallicence Linear regression nationallicence Finn G. Neurocomputing Research Centre, Queensland University of Technology, GPO Box 2434, Brisbane, Australia aut Lister R. Neurocomputing Research Centre, Queensland University of Technology, GPO Box 2434, Brisbane, Australia aut Szabo T. Neurocomputing Research Centre, Queensland University of Technology, GPO Box 2434, Brisbane, Australia aut Simonetta D. Queensland Department of Primary Industries, Brisbane, Australia aut Mulder H. Queensland Department of Primary Industries, Brisbane, Australia aut Young R. Queensland Department of Primary Industries, Brisbane, Australia aut Neural Computing & Applications Springer-Verlag 4/4(1996-12-01), 237-253 0941-0643 4:4<237 1996 4 521 https://doi.org/10.1007/BF01413822 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/BF01413822 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Finn G. Neurocomputing Research Centre, Queensland University of Technology, GPO Box 2434, Brisbane, Australia aut NATIONALLICENCE 700 1- Lister R. Neurocomputing Research Centre, Queensland University of Technology, GPO Box 2434, Brisbane, Australia aut NATIONALLICENCE 700 1- Szabo T. Neurocomputing Research Centre, Queensland University of Technology, GPO Box 2434, Brisbane, Australia aut NATIONALLICENCE 700 1- Simonetta D. Queensland Department of Primary Industries, Brisbane, Australia aut NATIONALLICENCE 700 1- Mulder H. Queensland Department of Primary Industries, Brisbane, Australia aut NATIONALLICENCE 700 1- Young R. Queensland Department of Primary Industries, Brisbane, Australia aut NATIONALLICENCE 773 0- Neural Computing & Applications Springer-Verlag 4/4(1996-12-01), 237-253 0941-0643 4:4<237 1996 4 521 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer