Search Results for author:
Found 2 papers, 0 papers with code
Feature selection in functional data classification with recursive maxima hunting
The results of an extensive empirical evaluation are used to illustrate that, in the problems investigated, RMH has comparable or higher predictive accuracy than the standard dimensionality reduction techniques, such as PCA and PLS, and state-of-the-art feature selection methods for functional data, such as maxima hunting.
The mRMR variable selection method: a comparative study for functional data
The mRMR (minimum Redundance Maximum Relevance) procedure, proposed by Ding and Peng (2005) and Peng et al. (2005) is an algorithm to systematically perform variable selection, achieving a reasonable trade-off between relevance and redundancy.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
