One is also learning a rule that may be applied to new instances. The notion of a rule as an abstract representation of a concept in the human mind came to be questioned by psychologists and there is still no good theory to explain how we store concepts. However, the same questions about the nature of representation arise in machine learning, for the choice of representation heavily determines the nature of a learning algorithm. Thus, one critical point of comparison among machine learning algorithms is the method of knowledge representation employed. Knowledge representation affects the speed of learning.
Some representations lend themselves to more efficient implementation than others.
Also, the more expressive the language, the larger is the search space. Knowledge representation determines the readability of the concept description. A representation that is opaque to the user may allow the program to learn, but a representation that is transparent also allows the user to learn. Instance-Based Learning Algorithms. A Theoretical Approach .