A New Discriminative Kernel From Probabilistic Models
Tsuda, Koji, Kawanabe, Motoaki, Rätsch, Gunnar, Sonnenburg, Sören, Müller, Klaus-Robert
–Neural Information Processing Systems
Recently, Jaakkola and Haussler proposed a method for constructing kernel functions from probabilistic models. Their so called "Fisher kernel" has been combined with discriminative classifiers such as SVM and applied successfully in e.g.
Neural Information Processing Systems
Dec-31-2002
- Country:
- Oceania > Australia
- Australian Capital Territory > Canberra (0.04)
- North America > United States
- California > San Diego County > San Diego (0.04)
- Europe
- United Kingdom > England
- Cambridgeshire > Cambridge (0.04)
- Germany
- Brandenburg > Potsdam (0.04)
- Berlin (0.04)
- United Kingdom > England
- Asia > Japan
- Honshū > Kantō > Tokyo Metropolis Prefecture > Tokyo (0.04)
- Oceania > Australia
- Genre:
- Research Report
- New Finding (0.68)
- Experimental Study (0.47)
- Research Report
- Technology: