---
_id: '2173'
abstract:
- lang: eng
  text: "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"
author:
- first_name: Sameh
  full_name: Khamis, Sameh
  last_name: Khamis
- first_name: Christoph
  full_name: Lampert, Christoph
  id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
  last_name: Lampert
  orcid: 0000-0001-8622-7887
citation:
  ama: 'Khamis S, Lampert C. CoConut: Co-classification with output space regularization.
    In: <i>Proceedings of the British Machine Vision Conference 2014</i>. BMVA Press;
    2014.'
  apa: 'Khamis, S., &#38; Lampert, C. (2014). CoConut: Co-classification with output
    space regularization. In <i>Proceedings of the British Machine Vision Conference
    2014</i>. Nottingham, UK: BMVA Press.'
  chicago: 'Khamis, Sameh, and Christoph Lampert. “CoConut: Co-Classification with
    Output Space Regularization.” In <i>Proceedings of the British Machine Vision
    Conference 2014</i>. BMVA Press, 2014.'
  ieee: 'S. Khamis and C. Lampert, “CoConut: Co-classification with output space regularization,”
    in <i>Proceedings of the British Machine Vision Conference 2014</i>, Nottingham,
    UK, 2014.'
  ista: 'Khamis S, Lampert C. 2014. CoConut: Co-classification with output space regularization.
    Proceedings of the British Machine Vision Conference 2014. BMVC: British Machine
    Vision Conference.'
  mla: 'Khamis, Sameh, and Christoph Lampert. “CoConut: Co-Classification with Output
    Space Regularization.” <i>Proceedings of the British Machine Vision Conference
    2014</i>, BMVA Press, 2014.'
  short: S. Khamis, C. Lampert, in:, Proceedings of the British Machine Vision Conference
    2014, BMVA Press, 2014.
conference:
  end_date: 2014-09-05
  location: Nottingham, UK
  name: 'BMVC: British Machine Vision Conference'
  start_date: 2014-09-01
date_created: 2018-12-11T11:56:08Z
date_published: 2014-09-01T00:00:00Z
date_updated: 2021-01-12T06:55:46Z
day: '01'
ddc:
- '000'
department:
- _id: ChLa
ec_funded: 1
file:
- access_level: open_access
  checksum: c4c6d3efdb8ee648faf3e76849839ce2
  content_type: application/pdf
  creator: system
  date_created: 2018-12-12T10:08:23Z
  date_updated: 2020-07-14T12:45:31Z
  file_id: '4683'
  file_name: IST-2016-490-v1+1_khamis-bmvc2014.pdf
  file_size: 408172
  relation: main_file
file_date_updated: 2020-07-14T12:45:31Z
has_accepted_license: '1'
language:
- iso: eng
month: '09'
oa: 1
oa_version: Published Version
project:
- _id: 2532554C-B435-11E9-9278-68D0E5697425
  call_identifier: FP7
  grant_number: '308036'
  name: Lifelong Learning of Visual Scene Understanding
publication: Proceedings of the British Machine Vision Conference 2014
publication_status: published
publisher: BMVA Press
publist_id: '4811'
pubrep_id: '490'
quality_controlled: '1'
scopus_import: 1
status: public
title: 'CoConut: Co-classification with output space regularization'
type: conference
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
year: '2014'
...