active learning Insights

What is active learning?

Active learning is a special case of machine learning in which a learning algorithm can interactively query a user (or other information source) to label new data points with the desired outputs. This approach is particularly useful when labeled data is scarce or expensive to obtain, as the algorithm can strategically select the most informative data points to label, thereby maximizing performance with minimal labeling effort. It is commonly used in scenarios such as image classification, text classification, and bioinformatics, where obtaining labels requires significant human effort or expert knowledge.

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What other technologies are related to active learning?

active learning Complementary Technologies

transfer learning

Transfer learning can be used to improve the efficiency of active learning by leveraging knowledge from pre-trained models.

mentioned alongside active learning in 9% (108) of relevant job posts

Gaussian processes

Gaussian processes can be used as a surrogate model in active learning to estimate the uncertainty of predictions.

mentioned alongside active learning in 16% (55) of relevant job posts

Bayesian optimization

Bayesian optimization is a sample-efficient optimization technique that uses a surrogate model to guide the search for the optimal solution, and is frequently used in active learning to select the most informative samples.

mentioned alongside active learning in 13% (53) of relevant job posts