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Learning conjunctions of Horn clauses
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Learning Conjunctive Concepts in Structural Domains
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Negative Results for Equivalence Queries
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Quantifying inductive bias: AI learning algorithms and Valiant's learning framework
inferredcites →https://doi.org/10.1016/0022-0000(90)90028-jopenalex-snapshot
Prediction-preserving reducibility
inferredcites →https://doi.org/10.1016/0304-3975(76)90068-2openalex-snapshot
Complete problems for deterministic polynomial time
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Learning Quickly When Irrelevant Attributes Abound: A New Linear-Threshold Algorithm
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/10.1109/sfcs.1988openalex-snapshot
[Proceedings 1988] 29th Annual Symposium on Foundations of Computer Science
inferredcites →https://doi.org/10.1109/sfcs.1988.21928openalex-snapshot
Predicting (0, 1)-functions on randomly drawn points
inferredcites →https://doi.org/10.1109/sfcs.1990openalex-snapshot
Proceedings [1990] 31st Annual Symposium on Foundations of Computer Science
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When won't membership queries help?
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Learning read-once formulas with queries
inferredcites →https://doi.org/10.1145/174644.174647openalex-snapshot
Cryptographic Limitations on Learning Boolean Formulae and Finite Automata
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On the learnability of Boolean formulae
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Crytographic limitations on learning Boolean formulae and finite automata
inferredcites →https://doi.org/10.1145/76359.76371openalex-snapshot
Learnability and the Vapnik-Chervonenkis dimension
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Learning decision trees from random examples
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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Derandomized learning of boolean functions
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Machine Learning for Software Analysis: Models, Methods, and Applications
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Complexity theoretic hardness results for query learning
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Optimizations in computing the Duquenne–Guigues basis of implications
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Construction and learnability of canonical Horn formulas
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Optimal compression of propositional Horn knowledge bases: complexity and approximation
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Pac-learning non-recursive prolog clauses
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Horn approximations of empirical data
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Recognition of q-Horn formulae in linear time
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Approximate inference of functional dependencies from relations
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Learning From Entailment: An Application to Propositional Horn Sentences
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Theory revision with queries: Horn, read-once, and parity formulas
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Learning conditional preference networks
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Learning constraints through partial queries
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Computational aspects of monotone dualization: A brief survey
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On the gap between ess(f) and cnf_size(f)
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Probably approximately correct learning of Horn envelopes from queries
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Polynomial certificates for propositional classes
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From equivalence queries to PAC learning: The case of implication theories
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Learning Horn envelopes via queries from language models
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Reasoning and learning in the setting of possibility theory - Overview and perspectives
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PAC learning of concept inclusions for ontology-mediated query answering
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An efficient algorithm for Horn description
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Learning a subclass of k-quasi-Horn formulas with membership queries
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Negative results on learning multivalued dependencies with queries
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Learning a circuit by injecting values
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A representation of antimatroids by Horn rules and its application to educational systems
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On learning multivalued dependencies with queries
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Learning definite Horn formulas from closure queries
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Hydras: Directed hypergraphs and Horn formulas
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Exact learning of multivalued dependency formulas
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Exact learning from an honest teacher that answers membership queries
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Copy complexity of Horn formulas with respect to unit read-once resolution
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Logical settings for concept-learning
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Translation among CNFs, characteristic models and ordered binary decision diagrams
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Learnability of quantified formulas
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Learning unions of tree patterns using queries
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Learning orthogonal F-Horn formulas
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Computational Learning Theory for Artificial Neural Networks
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A Dichotomy Theorem for Learning Quantified Boolean Formulas
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Learning Function-Free Horn Expressions
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On Learning Visual Concepts and DNF Formulae
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Remarks on computational learning theory
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Horn representation of a concept lattice
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Quasi-acyclic propositional Horn knowledge bases: optimal compression
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Graph-based methods for Horn knowledge compression
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Entailment among Probabilistic Implications
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Exact learning via the Monotone theory
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On Learning Read-k-Satisfy-j DNF
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The Inverse Satisfiability Problem
inferred← citeshttps://doi.org/10.1137/s0097539796297954openalex-snapshot
Horn Extensions of a Partially Defined Boolean Function
inferred← citeshttps://doi.org/10.1142/9789814329484_0006openalex-snapshot
HYPOTHESIS REFINEMENT: BUILDING HYPOTHESES IN AN INTELLIGENT AGENT SYSTEM
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DERANDOMIZED LEARNING OF BOOLEAN FUNCTIONS OVER FINITE ABELIAN GROUPS
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Learning a circuit by injecting values
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Asking questions to minimize errors
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On learning visual concepts and DNF formulae
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On learning Read-k-Satisfy-j DNF
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Learning with queries but incomplete information (extended abstract)
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Learning from a consistently ignorant teacher
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Learning DNF over the uniform distribution using a quantum example oracle
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How many queries are needed to learn?
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Learning and verifying quantified boolean queries by example
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A dichotomy theorem for learning quantified Boolean formulas
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Learning from examples with unspecified attribute values (extended abstract)
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Learning first order universal Horn expressions
inferred← citeshttps://doi.org/10.1145/279943.279980openalex-snapshot
Exact learning of tree patterns from queries and counterexamples
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The query complexity of finding local minima in the lattice
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Schema Independent Relational Learning
inferred← citeshttps://doi.org/10.1145/307400.307411openalex-snapshot
On theory revision with queries
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More theory revision with queries (extended abstract)
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Learning CNF Formulas from Uniform Random Solutions in the Local Lemma Regime
inferred← citeshttps://doi.org/10.1201/9781584888239-c26openalex-snapshot
Computational Learning Theory
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A Model for Learning Description Logic Ontologies Based on Exact Learning
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Generalized Planning with Positive and Negative Examples
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Translating between Horn Representations and their Characteristic Models
inferred← citeshttps://doi.org/10.23638/lmcs-15(1:10)2019openalex-snapshot
Relative Entailment Among Probabilistic Implications
inferred← citeshttps://doi.org/10.24963/ijcai.2020/259openalex-snapshot
On the Learnability of Possibilistic Theories
inferred← citeshttps://doi.org/10.24963/ijcai.2021/260openalex-snapshot
Actively Learning Concepts and Conjunctive Queries under ELr-Ontologies
inferred← citeshttps://doi.org/10.24963/kr.2022/17openalex-snapshot
Unique Characterisability and Learnability of Temporal Instance Queries
inferred← citeshttps://doi.org/10.26226/morressier.604907f51a80aac83ca25d95openalex-snapshot
Polynomial Identification of $\omega$-Automata
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Constraint Acquisition Based on Solution Counting
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Constructing Concise Characteristic Samples for Acceptors of Omega Regular Languages
inferred← citeshttps://doi.org/10.48550/arxiv.1503.09025openalex-snapshot
Learning Definite Horn Formulas from Closure Queries
inferred← citeshttps://doi.org/10.48550/arxiv.1504.07753openalex-snapshot
Hydras: Directed Hypergraphs and Horn Formulas
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Extremal Problems on Generalized Directed Hypergraphs
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Extremal Numbers for 2 to 1 Directed Hypergraphs with Two Edges Part I: The Nondegenerate Cases
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A linear programming model for sequential testing
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