---
res:
  bibo_abstract:
  - This paper focuses on the implementation details of the baseline methods and a
    recent lightweight conditional model extrapolation algorithm LIMES [5] for streaming
    data under class-prior shift. LIMES achieves superior performance over the baseline
    methods, especially concerning the minimum-across-day accuracy, which is important
    for the users of the system. In this work, the key measures to facilitate reproducibility
    and enhance the credibility of the results are described.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Paulina
      foaf_name: Tomaszewska, Paulina
      foaf_surname: Tomaszewska
  - 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
  bibo_doi: 10.1007/978-3-031-40773-4_6
  bibo_volume: 14068
  dct_date: 2023^xs_gYear
  dct_isPartOf:
  - http://id.crossref.org/issn/0302-9743
  - http://id.crossref.org/issn/1611-3349
  - http://id.crossref.org/issn/9783031407727
  dct_language: eng
  dct_publisher: Springer Nature@
  dct_title: On the implementation of baselines and lightweight conditional model
    extrapolation (LIMES) under class-prior shift@
...