Concept learning is how we learn to organize the world around us based on experience.
Concept learning describes the process by which experience allows us to partition objects in the world into classes for the purpose of generalization, discrimination, and inference. This means that through our interactions and observations, we learn to group different objects or ideas into categories.
Key Aspects of Concept Learning
Based on the definition, concept learning involves several core components:
- Experience: Learning happens through encountering examples and non-examples of a concept in the real world or through data.
- Partitioning Objects: The goal is to divide the vast number of objects or instances we encounter into distinct groups or "classes."
- Purpose: Why do we form these classes?
- Generalization: To apply knowledge learned about one member of a class to other members of the same class. For example, if you learn a specific bird can fly, you might generalize that other similar-looking birds can also fly.
- Discrimination: To differentiate between objects belonging to different classes. This helps us recognize that a bird is different from a fish.
- Inference: To make predictions or draw conclusions about new, unseen objects based on the class they belong to. If you see a new animal that looks like a dog, you infer it might bark or enjoy fetch.
Models that study concept learning have explored various ways these categories or concepts are represented internally. According to the reference, these models have adopted one of three contrasting views concerning category representation. While the specific views aren't detailed in this reference, it highlights that understanding how concepts are represented is a fundamental part of studying concept learning.
In essence, concept learning is the fundamental cognitive process (or machine learning task) of figuring out the rules or features that define a category, enabling us to sort and understand the world effectively.
