Support vector machines for predicting distribution of Sudden Oak Death in California

By Qinghua Guo, Maggi Kelly, Catherine H. Graham. 2005

345 citations recorded by the source corpus.

Subfields: Cell Biology, Ecology, Plant Science

View Paper: https://www.semanticscholar.org/paper/b6ba6ac4cef2d827b5765e38cef1f29ef737aa4c

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Citations in the corpus

On the selection of thresholds for predicting species occurrence with presence‐only data (2015, methodology): “Some techniques use presence data exclusively, for example, BIOCLIM (Busby 1991), DOMAIN (Carpenter et al. 1993), LIVES (Li and Hilbert 2008), and one-class SVM (Guo et al. 2005; Drake et al. 2006), while other techniques use both presence data and accompanying pseudo-absence data (often random poin”
Niches and distributional areas: Concepts, methods, and assumptions (2009, methodology): “Finally, presence-only data (without background absences) can be used by envelope techniques like BIOCLIM (73), support vector machines (74), or similarity methods like Mahalanobis distance classification (75), which simply surround presence points in environmental space with different geometrical s”
Exploring the effects of quantity and location of pseudo-absences and sampling biases on the performance of distribution models with limited point occurrence data (2011, background): “…inclusion of predictors able to represent the role played by the processes that hinder the presence of a species under environmentally favourable conditions can improve the results of the models under these circumstances (Kadmon et al. 2003; Guo et al. 2005; Chefaoui & Lobo 2008; Lobo et al. 2010).”
Performance evaluation and hyperparameter tuning of statistical and machine-learning models using spatial data (2018, methodology): “We decided to use the Radial Basis Function (RBF) kernel (also known as Gaussian kernel) which is the default in most implementations and most commonly used in the literature (Meyer et al., 2017; Guo et al., 2005; Pradhan, 2013).”
Focused identification of germplasm strategy (FIGS) detects wheat stem rust resistance linked to environmental variables (2011, background): “In this context, Hakes and Cronin (2011) assert that trait distribution patterns are not random and could be geographically and spatially structured.”
openModeller: a generic approach to species’ potential distribution modelling (2011, methodology): “These include various statistical approaches, like regression analysis [25–31], discriminant analysis [32, 33], and machine learning techniques [7, 9, 34–36].”
Predicting distributions of invasive species (2013, methodology): “ A modern machine learning method, the one-‐class support vector machine, has also been applied to modelling invasive species (Drake & Bossenbroek, 2009; Guo et al., 2005).”
Modelling distribution and abundance with presence‐only data (2006, methodology): “The two-class  model uses a similar approach to , except that it does not assume a particular probability distribution for the data (Guo et al. 2005).”
Support vector machine: A tool for mapping mineral prospectivity (2011, methodology): “Certain types of SVMs have been developed and applied successfully to text categorization, handwriting recognition, gene-function prediction, remote sensing classification and other studies (e.g., Joachims 1998; Huang et al.,2002; Cristianini and Scholkopf, 2002; Guo et al., 2005; Kavzoglu and Colke”
The n‐dimensional hypervolume (2014, background): “Similarly,other fast kernelbased approaches such as support vector machines (Guo et al., 2005) work in transformed high-dimensional spaces but had not been directly applied to geometry questions, as we show here.”