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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Ibba, A. Duin, R.P.W. Wan-Jui Lee |
| Copyright Year | 2010 |
| Abstract | The ways distances are computed or measured enable us to have different representations of the same objects. In this paper we want to discuss possible ways of merging different sources of information given by differently measured dissimilarity representations. We compare here a simple averaging scheme [1] with dissimilarity forward selection and other techniques based on the learning of weights of linear and quadratic forms. Our general conclusion is that, although the more advanced forms of combination cannot always lead to better classification accuracies, combining given distance matrices prior to training is always worthwhile. We can thereby suggest which combination schemes are preferable with respect to the problem data. |
| Starting Page | 3360 |
| Ending Page | 3363 |
| File Size | 443918 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781424475421 |
| ISSN | 10514651 |
| e-ISBN | 9781424475414 |
| DOI | 10.1109/ICPR.2010.820 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2010-08-23 |
| Publisher Place | Turkey |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Optimization Kernel Training Measurement Support vector machines Accuracy Artificial neural networks |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Vision and Pattern Recognition |
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