Machine Learning is a sub-area of artificial intelligence concerned with the induction of
knowledge or refinement of skill. It has attracted a lot of attention since the mid-1950's.
Computer programs that learn are called Machine Learning programs. These programs
develop concepts, infer new concepts from existing concepts, and revise incorrect
concepts. They have been used to create and maintain knowledge bases for many
applications.
Presented herewith is a sub-system of a learning program that concentrates on learning multi-dimensional data
types, specifically vector and matrix, and operators based on these data types. The system has been designed to
learn the multi-dimensional data types (MDDT) based on basic scalar data types. To avoid lengthy and
non-understandable sequence of learning steps, several powerful high-level methods have been developed to
facilitate the learning of operators for these MDDTs. The learning of the MDDT operators is based on these
basic methods and other basic scalar operators. An interface has been provided to demonstrate that these
operators have been learned correctly. Through this interface the user of the system is allowed to create
multi-dimensional data, and apply the learned operators on the created data.
The system, designed using the object oriented paradigm and implemented in C++,
performs consistency checks while learning and applying these MDDT operators. It also
performs consistency checks while creating multi-dimensional data.