Greedy feature constructionTools Oglic, Dino and Gaertner, Thomas (2016) Greedy feature construction. Advances in Neural Information Processing Systems, 29 . pp. 3945-3953. ISSN 1049-5258 Full text not available from this repository.AbstractWe present an effective method for supervised feature construction. The main goal of the approach is to construct a feature representation for which a set of linear hypotheses is of sufficient capacity -- large enough to contain a satisfactory solution to the considered problem and small enough to allow good generalization from a small number of training examples. We achieve this goal with a greedy procedure that constructs features by empirically fitting squared error residuals. The proposed constructive procedure is consistent and can output a rich set of features. The effectiveness of the approach is evaluated empirically by fitting a linear ridge regression model in the constructed feature space and our empirical results indicate a superior performance of our approach over competing methods.
Actions (Archive Staff Only)
|
Tools
Tools
