Inductive LearningIt is a method of machine learning, which is usually used for symbolic learning. It mainly summarizes a concept description from a series of known positive and negative examples about a concept.

Inductive learning can acquire new concepts, create new rules, and discover new theories. Its general operations are generalization and specialization. Generalization refers to expanding the semantic information of a hypothesis so that it can contain more positive examples for use in more situations; specialization is used to limit the scope of application of concept descriptions.

Inductive learning aims to summarize and extract general judgment rules and patterns from data experience, which can be regarded as a learning method to derive general rules from special cases.