@article { , title = {Greedy feature construction}, abstract = {We 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.}, conference = {30th Conference on Neural Information Processing Systems (NIPS 2016)}, eissn = {1049-5258}, journal = {Advances in Neural Information Processing Systems}, organization = {Barcelona, Spain}, publicationstatus = {Published}, publisher = {Massachusetts Institute of Technology Press}, url = {https://nottingham-repository.worktribe.com/output/836488}, volume = {29}, year = {2016}, author = {Oglic, Dino and Gaertner, Thomas} }