---
res:
  bibo_abstract:
  - "In this work we introduce a new approach to co-classification, i.e. the task
    of jointly classifying multiple, otherwise independent, data samples. The method
    we present, named CoConut, is based on the idea of adding a regularizer in the
    label space to encode certain priors on the resulting labelings. A regularizer
    that encourages labelings that are smooth across the test set, for instance, can
    be seen as a test-time variant of the cluster assumption, which has been proven
    useful at training time in semi-supervised learning. A regularizer that introduces
    a preference for certain class proportions can be regarded as a prior distribution
    on the class labels. CoConut can build on existing classifiers without making
    any assumptions on how they were obtained and without the need to re-train them.
    The use of a regularizer adds a new level of flexibility. It allows the integration
    of potentially new information at test time, even in other modalities than what
    the classifiers were trained on. We evaluate our framework on six datasets, reporting
    a clear performance gain in classification accuracy compared to the standard classification
    setup that predicts labels for each test sample separately.\r\n@eng"
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Sameh
      foaf_name: Khamis, Sameh
      foaf_surname: Khamis
  - 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
  dct_date: 2014^xs_gYear
  dct_language: eng
  dct_publisher: BMVA Press@
  dct_title: 'CoConut: Co-classification with output space regularization@'
...