---
res:
  bibo_abstract:
  - 'We introduce LIMES, a new method for learning with non-stationary streaming data,
    inspired by the recent success of meta-learning. The main idea is not to attempt
    to learn a single classifier that would have to work well across all occurring
    data distributions, nor many separate classifiers, but to exploit a hybrid strategy:
    we learn a single set of model parameters from which a specific classifier for
    any specific data distribution is derived via classifier adaptation. Assuming
    a multiclass classification setting with class-prior shift, the adaptation step
    can be performed analytically with only the classifier’s bias terms being affected.
    Another contribution of our work is an extrapolation step that predicts suitable
    adaptation parameters for future time steps based on the previous data. In combination,
    we obtain a lightweight procedure for learning from streaming data with varying
    class distribution that adds no trainable parameters and almost no memory or computational
    overhead compared to training a single model. Experiments on a set of exemplary
    tasks using Twitter data show that LIMES achieves higher accuracy than alternative
    approaches, especially with respect to the relevant real-world metric of lowest
    within-day accuracy.@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: 10.1109/icpr56361.2022.9956195
  bibo_volume: 2022
  dct_date: 2022^xs_gYear
  dct_identifier:
  - UT:000897707602018
  dct_isPartOf:
  - http://id.crossref.org/issn/2831-7475
  dct_language: eng
  dct_publisher: Institute of Electrical and Electronics Engineers@
  dct_title: Lightweight conditional model extrapolation for streaming data under
    class-prior shift@
...