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Abstract:
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 elds or structured out-
put SVMs, the proposed method has the advantage
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 eciently and robustly in situations
for which discriminative techniques
have computational or statistical problems.
