Concept and Concept Learning
Less than 500 views • Posted On Aug. 18, 2020
The concept is a Boolean-valued function defined over a large set of objects or events.
Concept Learning is defined as inferring a Boolean-valued function from training examples of input and output of the function.
Concept Learning can be represented using -
- Instance x: It is said to be a collection of attributes.
- Target function c: For example, X -> [0,1]
- Hypothesis h: Hypothesis h is a conjunction of constraints on the attributes. A constraint can be a specific value or no value at all.
- Training example d: An instance x(i) paired with the target function c.
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