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|Title:||PRINCIPLED INDUCTION FROM FEATURE VALUES|
|Abstract:||Principled induction is defined in this thesis as the process of arbitrarily selecting a valid description of a set of examples, and gradually simplifying this description until it is minimal in the preorder defined by the available simplification operators. If the preorder is well chosen, principled induction is a computationally feasible method for inducing descriptions. Algorithms are presented that perform principled induction of decision trees and rules from examples. These algorithms and empirical results obtained using them are shown to support the claim that principled induction is a clear and effective representation of the problem of learning concept descriptions from examples.|
|Appears in Collections:||Technical Reports|
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