Negative Results for Equivalence Queries
Machine Learning
Journal of Computer and System Sciences
On the power of equivalence queries
Euro-COLT '93 Proceedings of the first European conference on Computational learning theory
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)
Learning from examples with unspecified attribute values (extended abstract)
COLT '97 Proceedings of the tenth annual conference on Computational learning theory
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
General lower bounds on the query complexity within the exact learning model
Discrete Applied Mathematics - Special issue on Boolean functions and related problems
Machine Learning
Machine Learning
The Consistency Dimension and Distribution-Dependent Learning from Queries (Extended Abstract)
ALT '99 Proceedings of the 10th International Conference on Algorithmic Learning Theory
Abstract Combinatorial Characterizations of Exact Learning via Queries
COLT '00 Proceedings of the Thirteenth Annual Conference on Computational Learning Theory
ALT '01 Proceedings of the 12th International Conference on Algorithmic Learning Theory
A General Dimension for Approximately Learning Boolean Functions
ALT '02 Proceedings of the 13th International Conference on Algorithmic Learning Theory
The Complexity of Learning Concept Classes with Polynomial General Dimension
ALT '02 Proceedings of the 13th International Conference on Algorithmic Learning Theory
How many query superpositions are needed to learn?
ALT'06 Proceedings of the 17th international conference on Algorithmic Learning Theory
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We introduce a new combinatorial dimension that gives a good approximation of the number of queries needed to learn in the exact learning model, no matter what set of queries is used. This new dimension generalizes previous dimensions providing upper and lower bounds for all sorts of queries, and not for just example-based queries as in previous works. Our new approach gives also simpler proofs for previous results. We present specific applications of our general dimension for the case of unspecified attribute value queries, and show that unspecified attribute value membership and equivalence queries are not more powerful than standard membership and equivalence queries for the problem of learning DNF formulas.