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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Duch, W. Grudzinski, K. |
| Copyright Year | 1999 |
| Description | Author affiliation: Dept. of Comput. Methods, Nicholas Copernicus Univ., Torun, Poland (Duch, W.) |
| Abstract | The class of similarity based methods (SBM) covers most neural models and many other classifiers. Performance of such methods is significantly improved if irrelevant features are removed and feature weights introduced, scaling their influence on calculation of similarity. Several methods for feature selection and weighting are described. As an alternative to the global minimization procedures computationally efficient best-first search methods are advocated. Although these methods can be used with any SBM classifier they have been tested using the k-NN method since it is relatively fast and for some databases gives excellent results. A few illustrative examples show significant improvements due to the feature weighting and selection. |
| Starting Page | 3108 |
| Ending Page | 3113 |
| File Size | 656564 |
| Page Count | 6 |
| File Format | |
| ISBN | 0780355296 |
| ISSN | 10987576 |
| DOI | 10.1109/IJCNN.1999.836059 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 1999-07-10 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Minimization methods Neural networks Cost function Flexible manufacturing systems Search methods Testing Spatial databases Pattern recognition Machine learning Nearest neighbor searches |
| Content Type | Text |
| Resource Type | Article |
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