Case-based learning (CBL) is a machine learning method in which a system learns from solving previous cases similar to the current task.
In this method, the system uses a case base that contains a number of previously solved cases that are similar to the current task. The system uses this information to search for similar cases and apply the previous solution to the current task.
The CBL process consists of three phases: retrieval, adaptation and evaluation. In the retrieval phase, the system searches for similar cases in the database. In the adaptation phase, the system modifies the solution of the previous case to fit the current task. In the evaluation phase, the system evaluates the proposed solution and compares it with the optimal solution.
Case-based learning is used in a variety of applications, such as medical diagnostic problem solving, pattern recognition, decision making, task planning, among others.
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