Active Learning
Active learning (also called “query learning,” or sometimes “optimal experimental design” in the statistics literature) is a subfield of machine learning and, more generally, artificial intelligence. The key hypothesis is that, if the learning algorithm is allowed to choose the data from which it learns—to be “curious,” if you will—it will perform better with less training.
Senses
the process of learning through activities and/or discussion in class, as opposed to passively listening to an expert.
A proposed method for modifying machine learning algorithms by allowing them to specify test regions to improve their accuracy. At any point, the algorithm can choose a new point x, observe the output and incorporate the new (x, y) pair into its training base. It has been applied to neural networks, prediction functions, and clustering functions.
Classifications
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Variants
- plural
- Active Learnings
- possessive
- Active Learning's
- pluralpossessive
- Active Learnings'