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Exact Learning When Irrelevant Variables Abound by David Guijarro; Vı́ctor Lavı́n; Vijay Raghavan is a Computer Science article available to read on EtoBox.

What is Exact Learning When Irrelevant Variables Abound about?

We prove the following results. Any Boolean function of O(log n) relevant variables can be exactly learned with a set of nonadaptive membership queries alone and a minimum sized decision tree representation of the function constructed, in polynomial time. In contrast, such a function cannot be exactly learned with equivalence queries alone using general decision trees and other representation classes as hypotheses. Our results imply others which may be of independent interest. We show that truth-table minimization of decision trees can be done in polynomial time, complementing the well-known result of Masek that truth-table minimization of DNF formulas is NP-hard. The proofs of our negative results show that general decision trees and related representations are not learnable in polynomial time using equivalence queries alone, confirming a folklore theorem.

Who reads Exact Learning When Irrelevant Variables Abound?

Author

David Guijarro; Vı́ctor Lavı́n; Vijay Raghavan

Publisher

Elsevier Science; Elsevier ; Elsevier BV (ISSN 0020-0190)

Published

1999

Language

EN

Field

Computer Science (Physical Sciences)