3 Result(s) for ' high dimensional data'
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1.
Modelling Plant Growth Based on Gompertz, Logistic Curve, Extreme Gradient Boosting and Light Gradient Boosting Models Using High Dimensional Image Derived Maize (Zea mays L.) Phenomic Data
Peter Gachoki, Moses Muraya, Gladys Njoroge
American Journal of Applied Mathematics and Statistics. 2022 10 (2). doi: 10.12691/ajams-10-2-3
Keywords: Phenomic data, XGBoost, Light GBM, Gompertz model and logistic function model
Context: ...ake a long time under field conditions. Modelling of plant growth has been aggravated by new phenotyping platforms that generate high dimensional data non-destructively over the entire growth time of a plant using a set of camera system. Such platforms generate high-thr...
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2.
Characterisation of Academic Journal Publications Using Text Mining Techniques
Adebola K. Ojo, Adesesan B. Adeyemo
Journal of Computer Sciences and Applications. 2017 5 (2). doi: 10.12691/jcsa-5-2-1
Keywords: highly rated journals, text mining, self-organising maps, filtering and stemming algorithms
Context: ...ulted into information and cognitive overload. However, publication data are textual, unstructured and anomalous. Analysing such high dimensional data manually is time consuming and this has limited the ability to make projections and trends derivable from the patterns ...
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3.
Features Selection in Statistical Classification of High Dimensional Image Derived Maize (Zea Mays L.) Phenomic Data
Peter Gachoki, Moses Muraya, Gladys Njoroge
American Journal of Applied Mathematics and Statistics. 2022 10 (2). doi: 10.12691/ajams-10-2-2
Keywords: high throughput phenotyping, high dimensional data , feature extraction, feature importance, Shapley values, LASSO regression
Context: ...a single run. Hence, the need for parallel development of statistical techniques that can appropriately handle such large and/or high dimensional data set. Moreover, there is need to come up with a statistical criteria for selecting the best image derived phenotypic fea...
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