Solving categorization problem using two models of machine learning

Abstract EN

The ability to recognize quickly and accurately which we encounter is fundamental to normal intelligent human behavior.

However, how the learning of categories which objects in the world fit into takes place is still an unanswered question.

One thing is certain though ; much of the learning that takes place allows humans to cope with the changing they encounter.

One of the most important aspects of human intelligence is its flexibility which has allowed humans to prosper in a dynamic world.

Humans do not suffer from the ills of old fashioned hard rule based artificial intelligence.

The study tested six cubes.

The vertices of the cubes represent individual stimuli constructed from three binary dimensions.

The dimension of the stimuli can be assumed to correspond to shape (square vs.

circle), color (black vs.

white), and size (large vs.

small).

Four stimuli belonged to one category and the other four to a different category.

These constraints result in six problem types, which are illustrated by the six cubes.

The circle vertices represent stimuli that belong to category A, and the square vertices represent stimuli that belong to category B.

The faces of the cubes represent a constant value across one of the three dimensions that define the stimuli.

This work presents experiments with two different classifier systems: learning when fitness is based upon strength and specificity, and learning when fitness is based on strength alone.

The system is implemented using Pascal programming language.

Results show lower performance of the system when depending on strength alone.

By contrast, the run with strength and specificity allows a fast desired output.