Extending the learnability of decision trees

Abstract

The author concentrates on B. Natarajan's (1991) framework for learning classes of total functions of discrete domains. A. Ehrenfeucht and D. Haussler (1989) have shown that a subclass of decision trees is learnable in the sense defined by L. Valiant (1984). The author generalizes their definitions to m-ary domains and shows that the learnability of restricted decision tree classifiers carries over to the extended model.<>