Greedy feature construction
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 a...
| Main Authors: | , |
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| Format: | Article |
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Curran Associates Inc.
2016
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| Online Access: | https://eprints.nottingham.ac.uk/38608/ |
| _version_ | 1848795651400269824 |
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| author | Oglic, Dino Gaertner, Thomas |
| author_facet | Oglic, Dino Gaertner, Thomas |
| author_sort | Oglic, Dino |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T19:35:28Z |
| format | Article |
| id | nottingham-38608 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:35:28Z |
| publishDate | 2016 |
| publisher | Curran Associates Inc. |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-386082020-05-04T18:27:31Z https://eprints.nottingham.ac.uk/38608/ Greedy feature construction Oglic, Dino Gaertner, Thomas 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. Curran Associates Inc. 2016-12-05 Article PeerReviewed Oglic, Dino and Gaertner, Thomas (2016) Greedy feature construction. Advances in Neural Information Processing Systems, 29 . pp. 3945-3953. ISSN 1049-5258 http://papers.nips.cc/paper/6557-greedy-feature-construction |
| spellingShingle | Oglic, Dino Gaertner, Thomas Greedy feature construction |
| title | Greedy feature construction |
| title_full | Greedy feature construction |
| title_fullStr | Greedy feature construction |
| title_full_unstemmed | Greedy feature construction |
| title_short | Greedy feature construction |
| title_sort | greedy feature construction |
| url | https://eprints.nottingham.ac.uk/38608/ https://eprints.nottingham.ac.uk/38608/ |