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A novel two dimensional regression methodology was developed to curve-fit all of the FE results from this work.
Therefore four dimensional regression models were plotted against the input variables of temperature, electrical current density and the anode circuit's methanol concentration.
IFS has been widely used to solve high dimensional regression [ 65] and classification problems [ 66– 66].
Penalized likelihood methods can be applied to these high dimensional regression problems to perform model selection.
Network-based approaches have recently gained considerable popularity in high- dimensional regression settings.
Positioning Alus among the most relevant variables confirms our prior analyses based on 1 and 2 dimensional regression.
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The simulation below is a 1-dimensional regression where a neural network is trained to regress to y coordinates for every given point x through an L2 loss.
More recent applications include dimensionality reduction prior to gene set testing [ 12, 13] and high-dimensional regression [ 14].
Topic: "A Tuning-free Approach to High-dimensional Regression" Abstract: We introduce a new tuning-free approach for high-dimensional regression with theoretical guarantee.
"Sparse High-Dimensional Regression: Exact Scalable Algorithms and Phase Transitions". Bertsimas, Dimitris, and Bart P.G.
How to scale one of the main Bayesian models for sparse high-dimensional regression to hundreds of thousands of predictors.
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