Bo Stenberg
Artificial Intelligence · 2014
A researcher in the Science corpus: 2 papers; trusts 1 researcher and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 5 citations into it
Whom Bo Stenberg trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
Papers from Bo Stenberg's perspective
Ordered by how strongly the researchers Bo Stenberg trusts endorse each paper. Same corpus, a different order for every researcher.
- Spiking of NIR regional models using samples from target sites: effect of model size on prediction accuracy.
- Greedy function approximation: A gradient boosting machine.
- Selection and weighting of samples in multivariate regression model updating
- Optimization criteria in sample selection step of local regression for quantitative analysis of large soil NIRS database
- Validation requirements for diffuse reflectance soil characterization models with a case study of VNIR soil C prediction in Montana
- Soil parameter quantification by NIRS as a Chemometric challenge at ‘Chimiométrie 2006’
- Estimating forest soil bulk density using boosted regression modelling
- Smoothing and differentiation of data by simplified least square procedure.
- Stochastic gradient boosting
- Bagging Predictors
Their papers
- 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 (2014) — 146 citations
- Assessment of soil organic carbon at local scale with spiked NIR calibrations: effects of selection and extra‐weighting on the spiking subset (2014) — 116 citations