Cross-domain video concept detection using adaptive svms
Carnegie Mellon University · IBM (United States)
Abstract
Many multimedia applications can benefit from techniques for adapting existing classifiers to data with different distributions. One example is cross-domain video concept detection which aims to adapt concept classifiers across various video domains. In this paper, we explore two key problems for classifier adaptation: (1) how to transform existing classifier(s) into an effective classifier for a new dataset that only has a limited number of labeled examples, and (2) how to select the best existing classifier(s) for adaptation. For the first problem, we propose Adaptive Support Vector Machines (A-SVMs) as a general method to adapt one or more existing classifiers of any type to the new dataset. It aims to…
Citation impact
- FWCI
- 25.15
- Percentile
- 100%
- References
- 18
Authors
3Topics & keywords
- Classifier (UML)
- Computer science
- Support vector machine
- Domain adaptation
- Artificial intelligence
- Margin classifier
- Machine learning
- Pattern recognition (psychology)