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.
- 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.
- The process starts with initializing ‘h’ with the most specific hypothesis, generally, it is the first example in the dataset.
- We check for each positive example. If the example is negative, we will move on to the next example but if it a positive example we will consider it for the next step.
- We will check if each attribute in the example is equal to the hypothesis value.
- If the value matches, then no changes are made.
- If the value does not match, the value is changed to “?”.
- We do this until we reach the last positive example in the dataset.
Originally published at https://www.asquero.com/article/working-of-the-find-s-algorithm/
Classes of Problems in Machine Learning - https://www.asquero.com/article/classes-of-problems-in-machine-learning/
Concept and Concept Learning - https://www.asquero.com/article/concept-and-concept-learning/