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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Tommasi, T. Orabona, F. Caputo, B. |
| Copyright Year | 2010 |
| Description | Author affiliation: Idiap Research Institute, Martigny CH (Tommasi, T.; Caputo, B.) || DSI, Universita' degli Studi di Milano, Milan IT (Orabona, F.) |
| Abstract | Learning object categories from small samples is a challenging problem, where machine learning tools can in general provide very few guarantees. Exploiting prior knowledge may be useful to reproduce the human capability of recognizing objects even from only one single view. This paper presents an SVM-based model adaptation algorithm able to select and weight appropriately prior knowledge coming from different categories. The method relies on the solution of a convex optimization problem which ensures to have the minimal leave-one-out error on the training set. Experiments on a subset of the Caltech-256 database show that the proposed method produces better results than both choosing one single prior model, and transferring from all previous experience in a flat uninformative way. |
| Starting Page | 3081 |
| Ending Page | 3088 |
| File Size | 297649 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781424469840 |
| ISSN | 10636919 |
| e-ISBN | 9781424469857 |
| DOI | 10.1109/CVPR.2010.5540064 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2010-06-13 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Safety Knowledge transfer Machine learning Humans Databases Kernel Object recognition Adaptation model Machine learning algorithms Optimization methods |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Vision and Pattern Recognition Software |
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