Active Learning Literature Survey

Settles, B. (2009). Active Learning Literature Survey.

Type

Report

Intellectual base

Grey literature

Year

2009

Link

https://minds.wisconsin.edu/handle/1793/60660

Cited from

ethnography_qualitative_methods

Cited by

3 citations across 2 artifacts.

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

Primes

• Theoretical Sampling
  • Active learning in machine learning. A model selects which unlabeled examples to query by expected information gain — uncertainty sampling, query-by-committee, expected model change — with the model's current state determining which next examples are most informative.

    This sourceSurvey of active learning (uncertainty sampling, query-by-committee, expected model change) as model-driven selection of the next observation; traces its lineage to statistical optimal experimental design, not to grounded theory.

  • Structurally identical, and apparently arrived at independently: the active-learning literature traces its own ancestry to statistical theories of optimal experimental design rather than to any qualitative-research antecedent, so on the evidence of its standard survey the pair is a convergence on the same control loop rather than a borrowing.

    This sourceUniversity of Wisconsin–Madison Computer Sciences Technical Report 1648, 2009 — "This approach is derived from statistical theories of optimal experimental design (Federov, 1972)." Contradicts an earlier claim that the active-learning literature explicitly cites theoretical sampling as a conceptual ancestor: the strings "theoretical sampling", "grounded theory", "Glaser", "Strauss", "sociology", "qualitative" and "ethnograph" do not occur anywhere in the survey, which traces the lineage to optimal experimental design and PAC learning. No other source establishing the asserted citation lineage was found. This key is scoped to the lineage claim

Domain-specific

• Proactive learning
  • Settles's survey locates this problem within active learning, where selective label acquisition aims to reduce labeling effort while preserving model quality.

Verification

This reference passed the adversarial substantiation pipeline: it was checked to exist and to support the claim it is attached to. See how references were verified.

Links previously used in the corpus

Before the registry existed this work was also linked 1 other way.

• https://research.cs.wisc.edu/techreports/2009/TR1648.pdf ×1

Registry ID ref:529c676489a2 · see in the full table