TY  - GEN 
ID  - LampertB08
T1  - Joint Kernel Support Estimation for Structured Prediction
A1  - Lampert, Christoph
A1  - Blaschko, Matthew
Y1  - 2008
M1  - Conference or Workshop Item; PeerReviewed
UR  - http://eprints.pascal-network.org/archive/00004819/
N2  - We present a new technique for structured prediction that works in a hybrid generative/discriminative way, using a one-class support vector machine to model the joint probability of (input, output)-pairs in a joint reproducing kernel Hilbert space. Compared to discriminative techniques, like conditional random fields or structured output SVMs, the proposed method has the ad- vantage that its training time depends only on the number of training examples, not on the size of the label space. Due to its generative aspect, it is also very tolerant against ambiguous, incomplete or incorrect labels. Experiments on realistic data show that our method works efficiently and robustly in situations that discriminative techniques have problems with or that are computationally infeasible for them.
M1  - bibsource={OAI-PMH server at eprints.pascal-network.org}
M1  - 
M1  - 
subject={Learning/Statistics \& Optimisation; Theory \& Algorithms}
ER  -