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Prediction-based regularization using data augmented regression
Giles Hooker
*
,
Saharon Rosset
*
Corresponding author for this work
School of Mathematical Sciences
Cornell University College of Agriculture and Life Sciences
Research output
:
Contribution to journal
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Article
›
peer-review
5
Scopus citations
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Keyphrases
Prediction Method
100%
Data Augmentation
100%
Augmented Regression
100%
Control Model
25%
Performance Prediction
25%
Model Fitting
25%
Prediction Accuracy
25%
Linear Regression
25%
Nonparametric
25%
Feature Space
25%
Monte Carlo
25%
Asymptotic Analysis
25%
Model Complexity
25%
Range of Applicability
25%
Modeling Scenarios
25%
Curse of Dimensionality
25%
Nonparametric Representation
25%
Model Space
25%
Relevant Region
25%
High-dimensional Modeling
25%
K-nearest Neighbor Regression
25%
Pseudo Data
25%
Model Extrapolation
25%
Mathematics
Parametric
100%
Regularization
100%
Variance
50%
Real Data
25%
Monte Carlo
25%
Nearest Neighbor
25%
Asymptotic Analysis
25%
Simulated Data
25%
Predictive Performance
25%
Fitted Model
25%
Linear Regression
25%
Curse of Dimensionality
25%
Feature Space
25%
Predictive Accuracy
25%
Pseudo Data
25%
Model Space
25%
Relevant Region
25%
Engineering
Regularization
100%
Applicability
25%
Real Data
25%
Dimensional Modeling
25%
Dimensionality
25%
Feature Space
25%
Simulated Data
25%
Nearest Neighbor
25%
Parametric Representation
25%
Model Space
25%
Computer Science
Regularization
100%
Predictive Accuracy
25%
Feature Space
25%
Predictive Performance
25%
Model Complexity
25%
Dimensional Modeling
25%
Parametric Representation
25%
Psychology
Linear Regression
100%