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Randomly Fallible Teachers: Learning Monotone DNF with an Incomplete Membership Oracle

https://doi.org/10.1007/bf00993160
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Cryptographic limitations on learning Boolean formulae and finite automata
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Learning from Good and Bad Data
inferredcites →https://doi.org/10.1007/bf00116034openalex-snapshot
Negative Results for Equivalence Queries
inferredcites →https://doi.org/10.1007/bf00116035openalex-snapshot
Polynomial Time Learnability of Simple Deterministic Languages
inferredcites →https://doi.org/10.1007/bf00116827openalex-snapshot
Learning quickly when irrelevant attributes abound: A new linear-threshold algorithm
inferredcites →https://doi.org/10.1007/bf00116828openalex-snapshot
Queries and Concept Learning
inferredcites →https://doi.org/10.1007/bf00116829openalex-snapshot
Learning From Noisy Examples
inferredcites →https://doi.org/10.1007/bf00992675openalex-snapshot
Learning conjunctions of Horn clauses
inferredcites →https://doi.org/10.1016/0020-0190(91)90220-copenalex-snapshot
On learning from queries and counterexamples in the presence of noise
inferredcites →https://doi.org/10.1016/0890-5401(87)90052-6openalex-snapshot
Learning regular sets from queries and counterexamples
inferredcites →https://doi.org/10.1016/b978-1-55860-146-8.50006-0openalex-snapshot
LEARNING SWITCH CONFIGURATIONS
inferredcites →https://doi.org/10.1016/b978-1-55860-146-8.50034-5openalex-snapshot
Exact Identification of Circuits Using Fixed Points of Amplification Functions
inferredcites →https://doi.org/10.1016/b978-1-55860-213-7.50016-xopenalex-snapshot
Learning Monotone DNF with an Incomplete Membership Oracle
inferredcites →https://doi.org/10.1016/b978-1-55860-213-7.50019-5openalex-snapshot
On-line Learning with an Oblivious Environment and the Power of Randomization
inferredcites →https://doi.org/10.1023/a:1022869011914openalex-snapshot
Learning Quickly When Irrelevant Attributes Abound: A New Linear-Threshold Algorithm
inferredcites →https://doi.org/10.1109/fscs.1990.89538openalex-snapshot
Exact identification of circuits using fixed points of amplification functions
inferredcites →https://doi.org/10.1109/sfcs.1987.37openalex-snapshot
Learning quickly when irrelevant attributes abound: A new linear-threshold algorithm
inferredcites →https://doi.org/10.1145/103418.103420openalex-snapshot
When won't membership queries help?
inferredcites →https://doi.org/10.1145/103418.103443openalex-snapshot
Probabilistic recurrence relations
inferredcites →https://doi.org/10.1145/138027.138061openalex-snapshot
Learning read-once formulas with queries
inferredcites →https://doi.org/10.1145/1968.1972openalex-snapshot
A theory of the learnable
inferredcites →https://doi.org/10.1145/28395.28426openalex-snapshot
On the learnability of Boolean formulae
inferredcites →https://doi.org/10.1145/3749740.3749746openalex-snapshot
A Theory of the Learnable
inferredcites →https://doi.org/10.1145/62212.62238openalex-snapshot
Learning in the presence of malicious errors
inferredcites →https://doi.org/10.1145/73007.73049openalex-snapshot
Crytographic limitations on learning Boolean formulae and finite automata
inferredcites →https://doi.org/10.5555/114836.114849openalex-snapshot
Learning monotone DNF with an incomplete membership oracle
inferredcites →https://doi.org/10.5555/114836.114852openalex-snapshot
On-line learning with an oblivious environment and the power of randomization
inferredcites →https://doi.org/10.5555/92571.92578openalex-snapshot
Learning switch configurations
inferredcites →https://doi.org/10.5555/93025.93060openalex-snapshot
Types of noise in data for concept learning
inferredcites →https://doi.org/10.5555/93025.93062openalex-snapshot
Learning k-DNF with noise in the attributes

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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The Query Complexity of Finding Local Minima in the Lattice
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A Model of Interactive Teaching
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Learning with Unreliable Boundary Queries
inferred← citeshttps://doi.org/10.1007/3-540-44581-1_36openalex-snapshot
Learning Monotone DNF from a Teacher That Almost Does Not Answer Membership Queries
inferred← citeshttps://doi.org/10.1007/3-540-44581-1_38openalex-snapshot
Learning Regular Sets with an Incomplete Membership Oracle
inferred← citeshttps://doi.org/10.1007/3-540-45841-7_31openalex-snapshot
How Many Missing Answers Can Be Tolerated by Query Learners?
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Learning from incomplete boundary queries using split graphs and hypergraphs
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On exploiting knowledge and concept use in learning theory
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Learning Implications from Data and from Queries
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Omissions in Constraint Acquisition
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PExact = Exact Learning
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Separating Models of Learning with Faulty Teachers
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Exact Learning from Membership Queries: Some Techniques, Results and New Directions
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Classic learning
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Learning Fallible Deterministic Finite Automata
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On the Learnability of Disjunctive Normal Form Formulas
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How Many Missing Answers Can Be Tolerated by Query Learners?
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Efficient multiple constraint acquisition
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Supervised clustering using decision trees and decision graphs: An ecological comparison
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Learning with errors in answers to membership queries
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Separating models of learning with faulty teachers
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Exact learning from an honest teacher that answers membership queries
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Malicious Omissions and Errors in Answers to Membership Queries
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Classic Learning
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Circuit Learning for Logic Regression on High Dimensional Boolean Space
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Learning with Errors in Answers to Membership Queries (Extracted Abstract)
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Polynomial-sample learnability about distance-0 and 1 DNF formulas
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Exact learning via the Monotone theory
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Group-Based Active Query Selection for Rapid Diagnosis in Time-Critical Situations
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Learning with malicious membership queries and exceptions (extended abstract)
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Learning with queries but incomplete information (extended abstract)
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Learning with unreliable boundary queries
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Learning to reason
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Learning from examples with unspecified attribute values (extended abstract)
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The query complexity of finding local minima in the lattice
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A Relational Framework for Classifier Engineering
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