December 4, 1998

Maurício de Almeida
School of Information Technology and Engineering
University of Ottawa

Learning (Tree/Rule)-like boolean C++ methods

Decision trees and rule sets are commonly used languages to describe learned concepts. Even though those representations are easy to read and often the learners that generate them can evaluate their performance on the testing set, in the long run the rule sets and trees are not directly implementable in systems that are to use the learned rules or trees. This problem, among others, has suggested us an approach in which C++ classes, equivalent to a decision tree or a rule set, or a mixture of the two, are direct learned from a set of examples represented as attribute-value vectors. In this seminar we present the main ideas behind the Knowledge Embedding Learning sYstem, witch we are now implementing.

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