Negative Results for Equivalence Queries
Machine Learning
Weakly learning DNF and characterizing statistical query learning using Fourier analysis
STOC '94 Proceedings of the twenty-sixth annual ACM symposium on Theory of computing
Journal of Computer and System Sciences
Information and Computation
On the power of equivalence queries
Euro-COLT '93 Proceedings of the first European conference on Computational learning theory
Boosting a weak learning algorithm by majority
Information and Computation
Generalized teaching dimensions and the query complexity of learning
COLT '95 Proceedings of the eighth annual conference on Computational learning theory
How many queries are needed to learn?
Journal of the ACM (JACM)
An efficient membership-query algorithm for learning DNF with respect to the uniform distribution
Journal of Computer and System Sciences
General bounds on statistical query learning and PAC learning with noise via hypothesis boosting
Information and Computation
Efficient noise-tolerant learning from statistical queries
Journal of the ACM (JACM)
On learning in the presence of unspecified attribute values
COLT '99 Proceedings of the twelfth annual conference on Computational learning theory
Structural results about exact learning with unspecified attribute values
Journal of Computer and System Sciences - Eleventh annual conference on computational learning theory&slash;Twelfth Annual IEEE conference on computational complexity
The consistency dimension and distribution-dependent learning from queries
Theoretical Computer Science
Machine Learning
Machine Learning
Learning from examples with unspecified attribute values
Information and Computation
On using extended statistical queries to avoid membership queries
The Journal of Machine Learning Research
The complexity of learning concept classes with polynomial general dimension
Theoretical Computer Science - Algorithmic learning theory(ALT 2002)
Learning DNF by statistical and proper distance queries under the uniform distribution
ALT'05 Proceedings of the 16th international conference on Algorithmic Learning Theory
Query Learning and Certificates in Lattices
ALT '08 Proceedings of the 19th international conference on Algorithmic Learning Theory
A complete characterization of statistical query learning with applications to evolvability
Journal of Computer and System Sciences
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We introduce a combinatorial dimension that characterizes the number of queries needed to exactly (or approximately) learn concept classes in various models. Our general dimension provides tight upper and lower bounds on the query complexity for all sorts of queries, not only for example-based queries as in previous works. As an application we show that for learning DNF formulas, unspecified attribute value membership and equivalence queries are not more powerful than standard membership and equivalence queries. Further, in the approximate learning setting, we use the general dimension to characterize the query complexity in the statistical query as well as the learning by distances model. Moreover, we derive close bounds on the number of statistical queries needed to approximately learn DNF formulas.