In case-based learning, which is a concept often applied in machine learning, the system learns from individual instances or cases rather than relying solely on general rules or models. It’s a type of lazy learning approach where the system stores and uses specific instances to make predictions or decisions.

In the context of machine learning, case-based learning involves:

1. Case Representation: Each instance or case is represented by a set of attributes or features. These attributes describe the characteristics of the case.
2. Case Retrieval: When a new query or instance is presented to the system, it searches through its stored cases to find the most similar cases to the query.
3. Case Adaptation: The system adapts the information from retrieved cases to generate a prediction or solution for the new query.
4. Case Base Maintenance: The system might update its case base over time by adding new cases or removing outdated ones to improve its performance.

Case-based learning is particularly useful when there is a lack of clear rules or patterns that can be learned from data directly. It’s often employed in areas where domain knowledge and context play a significant role in making decisions. The success of case-based learning heavily depends on the quality of case representation, similarity measures, and the adaptability of retrieved cases to the new situations.