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Full-Text Articles in Artificial Intelligence and Robotics
Land-Surface Parameters For Spatial Predictive Mapping And Modeling, Aaron E. Maxwell, Charles Shobe
Land-Surface Parameters For Spatial Predictive Mapping And Modeling, Aaron E. Maxwell, Charles Shobe
Faculty & Staff Scholarship
Land-surface parameters derived from digital land surface models (DLSMs) (for example, slope, surface curvature, topographic position, topographic roughness, aspect, heat load index, and topographic moisture index) can serve as key predictor variables in a wide variety of mapping and modeling tasks relating to geomorphic processes, landform delineation, ecological and habitat characterization, and geohazard, soil, wetland, and general thematic mapping and modeling. However, selecting features from the large number of potential derivatives that may be predictive for a specific feature or process can be complicated, and existing literature may offer contradictory or incomplete guidance. The availability of multiple data sources and …
On The Use Of Minimum Penalties In Statistical Learning, Ben Sherwood, Bradley S. Price
On The Use Of Minimum Penalties In Statistical Learning, Ben Sherwood, Bradley S. Price
Faculty & Staff Scholarship
Modern multivariate machine learning and statistical methodologies estimate parameters of interest while leveraging prior knowledge of the association between outcome variables. The methods that do allow for estimation of relationships do so typically through an error covariance matrix in multivariate regression which does not scale to other types of models. In this article we proposed the MinPEN framework to simultaneously estimate regression coefficients associated with the multivariate regression model and the relationships between outcome variables using mild assumptions. The MinPen framework utilizes a novel penalty based on the minimum function to exploit detected relationships between responses. An iterative algorithm that …