Home / AIO Classes / MachineLearningTask / Active Learning

Active Learning

MachineLearningTask AIO:ActiveLearning NOT_REVIEWED

A machine learning task focused on methods that interactively query a user or another information source to label new data points with the desired outputs.

Ontology IRI: https://w3id.org/aio/ActiveLearning

Ontology Structure & Relationships

Parent Classes

AIO:MachineLearningTask (Machine Learning Task)

Branches

MachineLearningTask

Annotations & Sources

Synonyms:

Query Learning exact

Definition Sources:

https://en.wikipedia.org/wiki/Active_learning_(machine_learning)

Subsets:

https://w3id.org/aio/MachineLearningSubset

Curated AIMech Concepts Grounded to this Class

12 Concepts

Curated Concept	Kind	Mapping Relation	Rationale
Efficient Global Optimization `AIMech:EfficientGlobalOptimization`	Algorithms	related_match	Original EGO adaptively queries an objective and updates a data-derived response model, relating it to the pinned concept of querying an information source for outputs. Deterministic black-box optimization is not identical to a data-labeling task, and this qualified association imposes neither an annotation requirement nor an upstream subclass or exact-match assertion.
Entropy Search `AIMech:EntropySearch`	Algorithms	related_match	The pinned class describes interactive querying of an information source for outputs. This algorithm chooses informative objective evaluations to update its surrogate and optimizer belief. The relation records that query-learning association. It does not equate black-box optimization with annotation or pool-based classification, assert subclass membership, or identify the specific acquisition with the broad AIO class.
Max-value Entropy Search `AIMech:MaxValueEntropySearch`	Algorithms	related_match	The pinned class describes interactive querying of an information source for outputs. MES chooses expensive objective evaluations for information about a latent maximum. This relation records a query-learning association, without identifying the algorithm with annotation or pool-based classification, asserting subclass membership, or inheriting a neural architecture.
Multi-task Bayesian optimization `AIMech:MultiTaskBayesianOptimization`	Algorithms	related_match	The procedures choose informative input/task evaluations and obtain outputs from an information source. This query-learning association does not equate objective optimization with classification labeling or assert exact or subclass identity with the broad pinned task.
Output-space Predictive Entropy Search `AIMech:OutputSpacePredictiveEntropySearch`	Algorithms	related_match	The relationship is through choosing informative queries and obtaining their outputs. The pinned AIO term describes a labeling-oriented machine-learning task, whereas this algorithm selects objective evaluations for optimization. This is a related mapping, not exact equivalence or an asserted subclass, neural-network or layer relationship.
PESC-F `AIMech:PESCF`	Algorithms	related_match	The connection is choosing informative queries. The pinned AIO term is a labeling-oriented task, while this algorithm adapts the computation used to choose objective and constraint evaluations. The mapping is related only, without equivalence, subclass, network or layer inheritance.
Parallel predictive entropy search `AIMech:ParallelPredictiveEntropySearch`	Algorithms	related_match	PPES adaptively queries informative batches of objective evaluations. This relates to querying an information source, but scalar black-box optimization is not identical to the pinned task of labeling data for predictive learning. This qualified curator mapping asserts neither exact identity nor an upstream subclass relation.
Parallel predictive entropy search for multi-objective Bayesian optimization with constraints `AIMech:ParallelPredictiveEntropySearchForMultiObjectiveOptimizationWithConstraints`	Algorithms	related_match	PPESMOC adaptively queries informative batches of objective and constraint evaluations. This relates to querying an information source, but constrained multi-objective optimization is not identical to the pinned task of labeling data for predictive learning. This qualified curator mapping asserts neither exact identity nor an upstream subclass relation.
Predictive Entropy Search `AIMech:PredictiveEntropySearch`	Algorithms	related_match	The pinned class describes interactive querying of an information source for outputs. This algorithm selects informative objective evaluations using a surrogate and optimizer-location uncertainty. The relation records that query-learning association without identifying the method with annotation or pool-based classification, asserting subclass membership, or making the broad AIO class an exact optimizer identity.
Predictive entropy search for multi-objective Bayesian optimization `AIMech:PredictiveEntropySearchForMultiObjectiveOptimization`	Algorithms	related_match	PESMO adaptively chooses informative objective evaluations. This resembles the pinned task of querying an information source, but multi-objective optimization and Pareto-set inference are not identical to labeling data for predictive learning. The mapping is a qualified curator relation, not an exact match or an upstream subclass assertion.
Predictive entropy search for multi-objective Bayesian optimization with constraints `AIMech:PredictiveEntropySearchForMultiObjectiveOptimizationWithConstraints`	Algorithms	related_match	PESMOC adaptively queries informative objective and constraint evaluations. This relates to the pinned task of querying an information source, but constrained multi-objective optimization is not identical to labeling data for predictive learning. The mapping is a qualified curator assessment, not an exact match or an asserted upstream subclass relation.
Predictive Entropy Search with Constraints `AIMech:PredictiveEntropySearchWithConstraints`	Algorithms	related_match	The connection is choosing informative queries and obtaining their outputs. The pinned AIO term describes a labeling-oriented machine-learning task, whereas PESC chooses objective and constraint evaluations for optimization. This related mapping asserts neither exact equivalence nor a subclass, neural-network or layer relationship.

Import Provenance & Upstream Synchronization

Source Repo: AIO GitHub

Release Version: aio.json

Commit: 397851c547…

License: CC-BY-4.0

Imported At: 2026-10-10T07:34:10Z

SHA256: 8f741d71408c…

Source File: data/aio/ActiveLearning.yaml