Inductive Learning System

An Inductive Learning System is a Machine Learning System that is based on Statistical Inference and that implements an Inductive Learning Algorithm to solve an Inductive Learning Task.

• Example(s):
  • a Statistical Learning System,
  • …
  • Counter-Example(s):
  • an Active Learning System,
  • a Cumulative Learning System,
  • an Incremental Learning System.
• See: Inductive Logic Programming, Inductive Inference, Learning Task, Inductive Logic, Incremental Learning System, Cumulative Learning System, Active Learning.

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References

2017

• (Sammut & Webb, 2017) ⇒ (2017) "Inductive Learning". In: Sammut, C., Webb, G.I. (eds) "Encyclopedia of Machine Learning and Data Mining". Springer, Boston, MA.
  • QUOTE: Inductive learning is a subclass of machine learning that studies algorithms for learning knowledge based on statistical regularities. The learned knowledge typically has no deductive guarantees of correctness, though there may be statistical forms of guarantees.

2012

• (Fernau, 2012) ⇒ Fernau H. (2012). "Approximative Learning Vs. Inductive Learning". In: Seel N.M. (eds) Encyclopedia of the Sciences of Learning. Springer, Boston, MA
  • QUOTE: In the (mathematical) theory of learning, the term approximative learning is used in different meanings. To ease understanding the concepts, briefly recall what inductive learning means: upon receiving (positive or negative) evidence, the learner (often also called inference machine) formulates hypotheses that should, over time, (always) yield a correct one. This notion goes at least back to Gold, 1967. This concept leads to several natural questions:

1. What is a “correct hypothesis?” This can be answered on a purely syntactic level (leading, e.g., to the notion of EX[planatory]-learning) or on a more semantic level (behaviorally …

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