Johanna Wetterlind
Artificial Intelligence · 2011–2014
A researcher in the Science corpus: 3 papers; trusts 1 researcher and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 5 citations into it
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Papers from Johanna Wetterlind's perspective
Ordered by how strongly the researchers Johanna Wetterlind trusts endorse each paper. Same corpus, a different order for every researcher.
- Greedy function approximation: A gradient boosting machine.
- Spiking of NIR regional models using samples from target sites: effect of model size on prediction accuracy.
- 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
- The Nature of Statistical Learning Theory
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
- Clover cover crops under-sown in winter wheat increase yield of subsequent spring barley—Effect of N dose and companion grass (2011) — 119 citations