Source: Soil Discussions. Unidade: ESALQ
Subjects: ANÁLISE DO SOLO, APRENDIZADO COMPUTACIONAL, ESPECTROSCOPIA INFRAVERMELHA, REDES NEURAIS, SOLOS
ABNT
NG, Wartini et al. Estimation of effective calibration sample size using visible near infrared spectroscopy: deep learning vs machine learning. Soil Discussions, p. 1-21 (pré-print), 2019Tradução . . Disponível em: https://doi.org/10.5194/soil-2019-48. Acesso em: 20 maio 2024.APA
Ng, W., Minasny, B., Mendes, W. de S., & Dematte, J. A. M. (2019). Estimation of effective calibration sample size using visible near infrared spectroscopy: deep learning vs machine learning. Soil Discussions, 1-21 (pré-print). doi:10.5194/soil-2019-48NLM
Ng W, Minasny B, Mendes W de S, Dematte JAM. Estimation of effective calibration sample size using visible near infrared spectroscopy: deep learning vs machine learning [Internet]. Soil Discussions. 2019 ; 1-21 (pré-print).[citado 2024 maio 20 ] Available from: https://doi.org/10.5194/soil-2019-48Vancouver
Ng W, Minasny B, Mendes W de S, Dematte JAM. Estimation of effective calibration sample size using visible near infrared spectroscopy: deep learning vs machine learning [Internet]. Soil Discussions. 2019 ; 1-21 (pré-print).[citado 2024 maio 20 ] Available from: https://doi.org/10.5194/soil-2019-48