Sphere-packings, lattices, and groups
Sphere-packings, lattices, and groups
The nature of statistical learning theory
The nature of statistical learning theory
Modern Regression Methods
Sampling of Highly Correlated Data for Polynomial Regression and Model Discovery
IDA '01 Proceedings of the 4th International Conference on Advances in Intelligent Data Analysis
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This paper proposes a method based on the Minimum Message Length (MML) Principle for the task of discovering polynomial models up to the second order. The method is compared with a number of other selection criteria in the ability to, in an automated manner, discover a model given the generated data. Of particular interest is the ability of the methods to discover (1) second-order independent variables, (2) independent variables with weak causal relationships with the target variable given a small sample size, and (3) independent variables with weak links to the target variable but strong links from other variables which are not directly linked with the target variable. A common nonbacktracking search strategy has been developed and is used with all of the model selection criteria.