Concept learning is a subfield of machine learning that deals with the task of inferring a general concept from a set of examples or instances. The goal of concept learning is to find a hypothesis that best explains the relationship between the input and output of a target concept.
A hypothesis is a candidate function that represents the target concept. The hypothesis is evaluated based on how well it fits the training examples. The hypothesis that fits the training examples the best is selected as the final concept.
Concept learning is often used in situations where a human expert is not available or where the cost of manual labeling is prohibitive.
Difference from ML
Concept learning is a subfield of machine learning that focuses on the process of generalizing from examples to concepts. It involves the development of algorithms that can learn to identify patterns in data and use them to make predictions or decisions about new data.
In concept learning, the goal is to learn a description of a concept or category based on a set of training examples, where the concept is a function that maps inputs to outputs.
Machine learning involves the development of algorithms that can learn from data and improve their performance over time. This can include supervised learning, unsupervised learning, and reinforcement learning.