Mistake bounds and logarithmic linear-threshold learning algorithms
Mistake bounds and logarithmic linear-threshold learning algorithms
Learning k-term DNF formulas with an incomplete membership oracle
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
On-line learning of rectangles in noisy environments
COLT '93 Proceedings of the sixth annual conference on Computational learning theory
The weighted majority algorithm
Information and Computation
Randomly Fallible Teachers: Learning Monotone DNF with an Incomplete Membership Oracle
Machine Learning - Special issue on computational learning theory
On-line learning with malicious noise and the closure algorithm
Annals of Mathematics and Artificial Intelligence
Machine Learning
Machine Learning
A Note on Learning DNF Formulas Using Equivalence and Incomplete Membership Queries
AII '94 Proceedings of the 4th International Workshop on Analogical and Inductive Inference: Algorithmic Learning Theory
How Many Missing Answers Can Be Tolerated by Query Learners?
STACS '02 Proceedings of the 19th Annual Symposium on Theoretical Aspects of Computer Science
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We present results concerning the learning of Monotone DNF (MDNF) from Incomplete Membership Queries and Equivalence Queries. Our main result is a new algorithm that allows efficient learning of MDNF using Equivalence Queries and Incomplete Membership Queries with probability of p = 1 - 1/poly(n, t) of failing. Our algorithm is expected to make O((tn/1 - p)2) queries, when learning a MDNF formula with t terms over n variables. Note that this is polynomial for any failure probability p = 1 - 1/poly(n, t). The algorithm's running time is also polynomial in t, n, and 1/(1 - p). In a sense this is the best possible, as learning with p = 1-1/ω(poly(n, t)) would imply learning MDNF, and thus also DNF, from equivalence queries alone.