---
res:
  bibo_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 fields or structured output
    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
    efficiently and robustly in situations that discriminative techniques have problems
    with or that are computationally infeasible for them.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Christoph
      foaf_name: Lampert, Christoph
      foaf_surname: Lampert
      foaf_workInfoHomepage: http://www.librecat.org/personId=40C20FD2-F248-11E8-B48F-1D18A9856A87
    orcid: 0000-0001-8622-7887
  - foaf_Person:
      foaf_givenName: Matthew
      foaf_name: Blaschko, Matthew
      foaf_surname: Blaschko
  dct_date: 2008^xs_gYear
  dct_language: eng
  dct_publisher: Curran Associates, Inc.@
  dct_title: Joint kernel support estimation for structured prediction@
...