An Overview of Concept Drift Applications

Žliobaitė, I., Pechenizkiy, M., & Gama, J. (2016). An Overview of Concept Drift Applications. Big Data Analysis: New Algorithms for a New Society.

Type

Book chapter

Intellectual base

Review or monograph

Year

2016

DOI

10.1007/978-3-319-26989-4_4

ISBN

9783319269870

Link

https://doi.org/10.1007/978-3-319-26989-4_4

Cited from

systems_cybernetics

Cited by

1 citation across 1 artifact.

Each citation links to the sentence it supports in the citing article.

Primes

• Reference Cadence Exceeds Tracking Bandwidth
  • The retraining-cadence-versus-drift logic transferred from machine learning into online-retail personalization with the identical trade-off between compute cost and drift error.

    This sourceSurveys concept drift across applications including retail/personalization, framing the retraining-cadence-versus-drift trade-off (compute cost vs. staleness) that maps the bandwidth-vs-reference-cadence inequality onto deployed models.

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