# Inductive Learning Task

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An Inductive Learning Task is a Machine Learning Task that is based on Statistical Inference Theory.

**AKA:**Statistical Learning.**Context:**- It can be solved by a Inductive Learning System that implements Inductive Learning Algorithms.
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**Counter-Example(s)****See:**Inductive Logic Programming, Inductive Inference, Learning Task, Inductive Logic, Supervised Classification.

## 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:

- 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 …

### 2011

- (Sammut & Webb, 2011) ⇒ Claude Sammut (editor), and Geoffrey I. Webb (editor). (2011). “Inductive Learning.” In: (Sammut & Webb, 2011) p.529

### 1999

- (Domingos, 1996) ⇒ Pedro Domingos. (1996). “Unifying Instance-based and Rule-based Induction.” In: Machine Learning, 24(2). doi:10.1023/A:1018006431188
**Inductive learning**is the explicit or implicit creation of general concept or class descriptions from examples. Many induction problems can be described as follows. A training set of preclassified examples is given, where each example (also called observation or case) is described by a vector of features or attribute values, and the goal is to form a description that can be used to classify previously unseen examples with high accuracy.