Improving the prediction performance of a large tropical vis‐NIR spectroscopic soil library from Brazil by clustering into smaller subsets or use of data mining calibration techniques
By Suzana Romeiro Araújo, Johanna Wetterlind, José Alexandre Mello Demattê, Bo Stenberg. 2014
146 citations recorded by the source corpus.
Subfields: Analytical Chemistry, Artificial Intelligence, Environmental Engineering
View Paper: https://www.semanticscholar.org/paper/00f6da4ceddc84e80fb2dff193151521da0ce84c
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Analytical Chemistry
In Artificial Intelligence
In Environmental Engineering
Citations in the corpus
- Two preprocessing techniques to reduce model covariables in soil property predictions by Vis-NIR spectroscopy (2017, methodology): “The SVM acceptance in soil properties estimation has increased in recent years (Araújo et al., 2014; Ramirez-Lopez et al., 2013; Terra et al., 2015) and has generated more accurate calibration results than PLSR in some studies (Thissen et al., 2004; Viscarra Rossel et al., 2010).”