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  Joint Kernel Support Estimation for Structured Prediction

Lampert, C., & Blaschko, M. (2008). Joint Kernel Support Estimation for Structured Prediction. In NIPS 2008 Workshop: Structured Input - Structured Output (NIPS SISO 2008) (pp. 1-4).

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NIPS-SISO-2008-Lampert.pdf (Any fulltext), 173KB
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 Creators:
Lampert, CH1, 2, Author           
Blaschko, M1, 2, Author           
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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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.

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 Dates: 2008-12
 Publication Status: Published in print
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 Rev. Type: -
 Identifiers: BibTex Citekey: 5610
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Title: NIPS 2008 Workshop: Structured Input - Structured Output (NIPS SISO 2008)
Place of Event: Whistler, BC, Canada
Start-/End Date: 2008-12-13

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Title: NIPS 2008 Workshop: Structured Input - Structured Output (NIPS SISO 2008)
Source Genre: Proceedings
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Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 1 - 4 Identifier: -