# Computational Complexity of Learning

**DOI:**https://doi.org/10.1007/978-0-387-30164-8_151

## Definition

Measures of the complexity of learning have been developed for a number of purposes including Inductive Inference, PAC Learning, and Query-Based Learning. The complexity is usually measured by the largest possible usage of ressources that can occur during the learning of a member of a class. Depending on the context, one measures the complexity of learning either by a single number/ordinal for the whole class or by a function in a parameter *n*describing the complexity of the target to be learnt. The actual measure can be the number of mind changes, the number of queries submitted to a teacher, the number of wrong conjectures issued, the number of errors made or the number of examples processed until learning succeeds. In addition to this, one can equip the learner with an oracle and determine the complexity of the oracle needed to perform the learning process. Alternatively, in complexity theory, instead of asking for an NP-complete oracle to learn a certain class, the...