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Declarative Bias for Specific-to-General ILP Systems

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What this work says

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

inferredcites →https://doi.org/10.1007/3-540-56602-3_130openalex-snapshot
Subsumption and refinement in model inference
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On the utility of predicate invention in inductive logic programming
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Representing biases for Inductive Logic Programming
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Biases and their effects in Inductive Logic Programming
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A Sketch of Autonomous Learning using Declarative Bias
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Machine Learning of Inductive Bias
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Learning Logical Definitions from Relations
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Interactive concept-learning and constructive induction by analogy
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The Utility of Knowledge in Inductive Learning
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Generalization as search
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Belief updating from integrity constraints and queries
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Grammatically biased learning: Learning logic programs using an explicit antecedent description language
inferredcites →https://doi.org/10.1016/0743-1066(94)90035-3openalex-snapshot
Inductive Logic Programming: Theory and methods
inferredcites →https://doi.org/10.1016/s0020-7373(87)80084-6openalex-snapshot
Induction of Horn clauses: methods and the plausible generalization algorithm
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A unifying framework for concept-learning algorithms
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Interactive Concept-Learning and Constructive Induction by Analogy
inferredcites →https://doi.org/10.1073/pnas.89.23.11322openalex-snapshot
Drug design by machine learning: the use of inductive logic programming to model the structure-activity relationships of trimethoprim analogues binding to dihydrofolate reductase.
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THE UTILITY OF BACKGROUND KNOWLEDGE IN LEARNING MEDICAL DIAGNOSTIC RULES
inferredcites →https://doi.org/10.1093/protein/5.7.647openalex-snapshot
Protein secondary structure prediction using logic-based machine learning
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PAC-learnability of determinate logic programs
inferredcites →https://doi.org/10.5860/choice.32-2788openalex-snapshot
Simply logical: intelligent reasoning by example
inferredcites →https://doi.org/10.7551/mitpress/1192.001.0001openalex-snapshot
Algorithmic Program Debugging

What points at this work

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

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RUTH: an ILP theory revision system
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A guided tour through hypothesis spaces in ILP
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Inductive constraint logic
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DLAB: A declarative language bias formalism
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Handling Quantifiers in ILP
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Using abstraction schemata in inductive logic programming
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Learning Single Rules
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A General Framework for Supporting Relational Concept Learning
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Fast rule mining in ontological knowledge bases with AMIE $$+$$ +
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For Every Generalization Action, Is There Really an Equal and Opposite Reaction? Analysis of the Conservation Law for Generalization Performance
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Clausal Discovery
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Integrating Multiple Learning Strategies in First Order Logics
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Phase Transitions in Relational Learning
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A knowledge-level model of a configurable learning system
inferred← citeshttps://doi.org/10.1109/tevc.2002.800882openalex-snapshot
NOW G-Net: learning classification programs on networks of workstations
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AUTOMATICALLY EXPLORING HYPOTHESES ABOUT FAULT PREDICTION: A COMPARATIVE STUDY OF INDUCTIVE LOGIC PROGRAMMING METHODS
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Testing by means of inductive program learning
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AMIE
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Learning atomic formulas with prescribed properties
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Relational concept learning by cooperative evolution
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Inductive Logic Programming At 30: A New Introduction
inferred← citeshttps://doi.org/10.3233/aic-1995-8101openalex-snapshot
Inductive Logic Programming: A Survey of European Research
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