Learning regular sets from queries and counterexamples
Information and Computation
Types of noise in data for concept learning
COLT '88 Proceedings of the first annual workshop on Computational learning theory
On learning from queries and counterexamples in the presence of noise
Information Processing Letters
Topics in the theory of machine learning and neural computing
Topics in the theory of machine learning and neural computing
Learning k-term DNF formulas with an incomplete membership oracle
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Learning read-once formulas with queries
Journal of the ACM (JACM)
Lower Bound Methods and Separation Results for On-Line Learning Models
Machine Learning - Computational learning theory
Learning Conjunctions of Horn Clauses
Machine Learning - Computational learning theory
Linear time deterministic learning of k-term DNF
COLT '93 Proceedings of the sixth annual conference on Computational learning theory
Learning with malicious membership queries and exceptions (extended abstract)
COLT '94 Proceedings of the seventh annual conference on Computational learning theory
Randomly Fallible Teachers: Learning Monotone DNF with an Incomplete Membership Oracle
Machine Learning - Special issue on computational learning theory
Simulating access to hidden information while learning
STOC '94 Proceedings of the twenty-sixth annual ACM symposium on Theory of computing
Learning Boolean read-once formulas over generalized bases
Journal of Computer and System Sciences
Machine Learning
Machine Learning
Learning with malicious membership queries and exceptions (extended abstract)
COLT '94 Proceedings of the seventh annual conference on Computational learning theory
Learning with unreliable boundary queries
COLT '95 Proceedings of the eighth annual conference on Computational learning theory
DNF—if you can't learn'em, teach'em: an interactive model of teaching
COLT '95 Proceedings of the eighth annual conference on Computational learning theory
Malicious Omissions and Errors in Answers to Membership Queries
Machine Learning
Learning from examples with unspecified attribute values (extended abstract)
COLT '97 Proceedings of the tenth annual conference on Computational learning theory
Learning Regular Sets with an Incomplete Membership Oracle
COLT '01/EuroCOLT '01 Proceedings of the 14th Annual Conference on Computational Learning Theory and and 5th European Conference on Computational Learning Theory
Learning attribute-efficiently with corrupt oracles
Theoretical Computer Science
Active learning with multiple views
Journal of Artificial Intelligence Research
Learning attribute-efficiently with corrupt oracles
ALT'05 Proceedings of the 16th international conference on Algorithmic Learning Theory
| Hi-index | 0.00 |
We investigate learning with membership and equivalence queries assuming that the information provided to the learner is incomplete. By incomplete we mean that some of the membership queries may be answered by “I don't know.” This model is a worst-case version of the incomplete membership query model of Angluin and Slonim. It attempts to model practical learning situations, including an experiment of Lang and Baum that we describe, where the teacher may be unable to answer reliably some queries that are critical for the learning algorithm.We present algorithms to learn monotone k-term DNF with membership queries only, and to learn monotone DNF with membership and equivalence queries. Compared to the complete information case, the query complexity increases by an additive term linear in the number of “I don't know” answers received. We also observe that the blowup in the number of queries can in general be exponential for both our new model and the incomplete membership model.