Inductive Learning Algorithm

An Inductive Learning Algorithm is a Machine Learning Algorithm that is implemented by an Inductive Learning System solve an Inductive Learning Task.

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