Name
Title | ||
|---|---|---|
Safety In Numbers: Learning Categories From Few Examples With Multi Model Knowledge Transfer |
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. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/CVPR.2010.5540064 | 2010 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR) |
Keywords | Field | DocType |
support vector machines,learning artificial intelligence,convex programming,data models,object recognition,machine learning,databases,mathematical model,optimization,convex optimization,kernel | Kernel (linear algebra),Data modeling,Pattern recognition,Computer science,Support vector machine,Knowledge transfer,Learning object,Artificial intelligence,Safety in numbers,Convex optimization,Machine learning,Cognitive neuroscience of visual object recognition | Conference |
Volume | Issue | ISSN |
2010 | 1 | 1063-6919 |
Citations | PageRank | References |
66 | 3.44 | 17 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tatiana Tommasi | 1 | 502 | 29.31 |
| Francesco Orabona | 2 | 881 | 51.44 |
| Barbara Caputo | 3 | 3298 | 201.26 |