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Uncertainty Threshold

Critical parameter determining the minimum level of uncertainty required for a stream sample to be selected for labeling by the human oracle.

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Sampling Strategy

Algorithmic methodology used to optimally select instances from the stream deserving manual labeling according to predefined criteria.

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Stream Classification

Process of assigning labels to sequentially arriving instances, with real-time and limited memory constraints.

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Instance Selection

Discriminating mechanism identifying the most informative samples from the stream to maximize model improvement per unit of labeling.

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Labeling Oracle

External source of information, typically human, providing correct labels for instances selected by the active learning system.

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Online Evaluation

Method of continuously evaluating model performance measuring accuracy or other metrics as new instances arrive and are processed.

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Temporal Adaptation

System's ability to dynamically modify its parameters and strategies to adjust to temporal evolutions of stream characteristics.

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Exploration-Exploitation Trade-off

Fundamental dilemma consisting of arbitrating between exploring new regions of the space and exploiting acquired knowledge to optimize learning.

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Labeling Latency

Temporal delay between the labeling request for an instance and the reception of the corresponding label, directly impacting streaming performance.

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Stream Density

Measure characterizing the arrival frequency of samples in the stream, directly influencing selection strategies and labeling budget.

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Sample Prioritization

Ranking algorithm for instances in the stream according to their expected information value to optimize labeling budget allocation.

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Incremental Update

Technique for progressively adding new knowledge to the existing model without requiring complete retraining from scratch.

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Rejection Strategy

Policy defining the conditions under which the system refuses to classify an instance due to excessive uncertainty or limited resources.

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Acquisition Cost

Temporal, financial, or computational resources required to obtain a label from the oracle in an active learning context.

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