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Inductive Inference

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Encyclopedia of Machine Learning and Data Mining

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Definition

Inductive inference is a theoretical framework to model learning in the limit. The typical scenario is that the learner reads successively datum d0, d1, d2, … about a concept and outputs in parallel hypotheses e0, e1, e2, … such that each hypothesis e n is based on the preceding data d0, d1, …, dn−1. The hypotheses are expected to converge to a description for the data observed; here the constraints on how the convergence has to happen depend on the learning paradigm considered. In the most basic case, almost all e n have to be the same correct index e, which correctly explains the target concept. The learner might have some preknowledge of what the concept might be, that is, there is some class \(\mathcal{C}\) of possible target concepts – the learner has only to find out which of the concepts in \(\mathcal{C}\) is the target concept; on the other hand, the learner has to be able to learn every concept which is in the class \(\mathcal{C}\).

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The above given scenario...

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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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Authors and Affiliations

  1. School of Computing, National University of Singapore, Singapore, Singapore

    Sanjay Jain

  2. Department of Mathematics, National University of Singapore, Singapore, Singapore

    Frank Stephan

Authors
  1. Sanjay Jain
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  2. Frank Stephan
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Correspondence to Sanjay Jain .

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Editors and Affiliations

  1. The University of New South Wales, Sydney, NSW, Australia

    Claude Sammut

  2. Faculty of Information Technology, Monash University, Melbourne, VIC, Australia

    Geoffrey I. Webb

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Jain, S., Stephan, F. (2017). 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_134

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