Solving Categorization Problem using Two Models of Machine Learning

Abstract

Abstract-The ability to recognize quickly and accurately which we encounter isfundamental to normal intelligent human behavior. However, how the learning ofcategories 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 tocope with the changing they encounter. One of the most important aspects of humanintelligence 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 artificialintelligence. The study tested six cubes. The vertices of the cubes represent individualstimuli constructed from three binary dimensions. The dimension of the stimuli can beassumed 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 differentcategory. These constraints result in six problem types, which are illustrated by the sixcubes. The circle vertices represent stimuli that belong to category A, and the squarevertices represent stimuli that belong to category B. The faces of the cubes represent aconstant value across one of the three dimensions that define the stimuli. This workpresents experiments with two different classifier systems: learning when fitness isbased upon strength and specificity, and learning when fitness is based on strengthalone. The system is implemented using Pascal programming language. Results showlower performance of the system when depending on strength alone. By contrast, therun with strength and specificity allows a fast desired output.