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Concept Learning & Find-S algorithm in Machine Learning

srajangupta__ profile image Srajan Gupta ・1 min read

Concept

The concept is a Boolean-valued function defined over a large set of objects or events.

Concept Learning

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.

The step-wise working of the find-S algorithm is given as-

  • 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/

Also Read-

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/

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