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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Chernousova, E. Levdik, P. Tatarchuk, A. Mottl, V. Windridge, D. |
| Copyright Year | 2014 |
| Description | Author affiliation: Moscow Inst. of Phys. & Technol., Moscow, Russia (Chernousova, E.; Levdik, P.) || Comput. Center, Moscow, Russia (Tatarchuk, A.; Mottl, V.) || Univ. of Surrey, Guildford, UK (Windridge, D.) |
| Abstract | The relational approach to dependency estimation entails the selection of a sufficiently compact 'relevance' subset of training-set objects with which any newly occurring object may be compared in order to estimate its hidden target characteristics. If several comparison modalities are available, a 'relevance' subset of these may additionally have to be chosen via an appropriate selection criterion. Typically, the level of selectivity will constitute a free parameter, and in traditional approaches, multiple training repetitions would be required to determine this value via cross-validation. To avoid this, we seek to algorithmically emulate the cross-validation process using conservative assumptions as to the nature of the unknown probability distribution that produced the training set. We term this approach 'non-enumerative cross-validation', and demonstrate that the classical Akaike Information Criterion is a specific case of it under naive assumptions. The application of this non-enumerative cross-validation strategy is demonstrated on the standard multikernel data set, "chicken-pieces", treated from the perspective of relational discriminant analysis. |
| Starting Page | 3654 |
| Ending Page | 3659 |
| File Size | 2695433 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781479952090 |
| ISSN | 10514651 |
| DOI | 10.1109/ICPR.2014.628 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2014-08-24 |
| Publisher Place | Sweden |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Support vector machines Training Vectors Structural engineering Observers Standards non-enumerative model verification relational dependence estimation relevance vector machine support vector machine feature selection selectivity adjustment Akaike information criterion non-enumerative cross-validation |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Vision and Pattern Recognition |
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