Definition
In inductive inference, the complexity of learning can be measured in various ways: by the number of hypotheses issued in the worst case until the correct hypothesis is found, by the number of data items to be consumed or to be memorized in order to learn in the worst case, by the Turing degree of oracles needed to learn the class under a certain criterion, and by the intrinsic complexity which is – like the Turing degrees in recursion theory – a way to measure the complexity of classes by using reducibilities between them.
Detail
We refer the reader to the article Inductive Inference for basic definitions in inductive inference and the notations used below. Let \(\mathbb{N}\) denote the set of natural numbers. Let φ0, φ1, … denote a fixed acceptable numbering of the partial-recursive functions (Rogers 1967). Let W i = domain(φ i ).
Mind Changes and Anomalies
The first measure of complexity of learning can be considered as the number of mind changes needed before the learner...
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Acknowledgements
Sanjay Jain was supported in part by NUS grant numbers C252-000-087-001, R146-000-181-112, R252-000-534-112. Frank Stephen was supported in part by NUS grant numbers R146-000-181-112, R252-000-534-112.
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Jain, S., Stephan, F. (2017). Complexity of Inductive Inference. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning and Data Mining. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7687-1_46
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DOI: https://doi.org/10.1007/978-1-4899-7687-1_46
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