Active Learning Task

From GM-RKB
Jump to: navigation, search

An Active Learning Task is an iterative supervised learning task that allows request for additional labeled training records from a learning record set.



References

2017a

2017b

2017c

  • (Wikipedia, 2017) ⇒ https://en.wikipedia.org/wiki/Active_learning_(machine_learning) Retrieved:2017-12-24.
    • Active learning is a special case of semi-supervised machine learning in which a learning algorithm is able to interactively query the user (or some other information source) to obtain the desired outputs at new data points. In statistics literature it is sometimes also called optimal experimental design.

      There are situations in which unlabeled data is abundant but manually labeling is expensive. In such a scenario, learning algorithms can actively query the user/teacher for labels. This type of iterative supervised learning is called active learning. Since the learner chooses the examples, the number of examples to learn a concept can often be much lower than the number required in normal supervised learning. With this approach, there is a risk that the algorithm be overwhelmed by uninformative examples.

      Recent developments are dedicated to multi-label active learning, hybrid active learning and active learning in a single-pass (on-line) context, combining concepts from the field of Machine Learning (e.g., conflict and ignorance) with adaptive, incremental learning policies in the field of Online machine learning.

2010

2008