DocumentCode
2366530
Title
Exact learning via the Monotone theory
Author
Bshouty, Nader H.
Author_Institution
Dept. of Comput. Sci., Calgary Univ., Alta., Canada
fYear
1993
fDate
3-5 Nov 1993
Firstpage
302
Lastpage
311
Abstract
We study the learnability of concept classes from membership and equivalence queries. We develop the Monotone theory that proves (1) Any boolean function is learnable as decision tree. (2) Any boolean function is either learnable as DNF or as CNF (or both). The first result solves the open problem of the learnability of decision trees and the second result gives more evidence that DNFs are not “very hard” to learn
Keywords
Boolean functions; learning (artificial intelligence); CNF; DNF; Monotone theory; boolean function; concept classes; decision trees; equivalence queries; exact learning; learnability; membership; Boolean functions; Circuits; Decision trees; Machine learning; Noise measurement; Polynomials;
fLanguage
English
Publisher
ieee
Conference_Titel
Foundations of Computer Science, 1993. Proceedings., 34th Annual Symposium on
Conference_Location
Palo Alto, CA
Print_ISBN
0-8186-4370-6
Type
conf
DOI
10.1109/SFCS.1993.366857
Filename
366857
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