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Inference of a minimum size boolean function from examples by using a new efficient branch-and-bound approach
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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An Overview of Machine Learning
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Machine learning: An artificial intelligence approach
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Generating logical expressions from positive and negative examples via a branch-and-bound approach
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Logic-based decision support
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Journal of the Association for Computing Machinery
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A Method of Producing a Boolean Function Having an Arbitrarily Prescribed Prime Implicant Table
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On the learnability of Boolean formulae
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Inductive Inference: Theory and Methods
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Computational limitations on learning from examples
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A Relationship Between CNF and DNF Systems Derivable from Examples
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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The One Clause at a Time (OCAT) Approach to Data Mining and Knowledge Discovery
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Discovering Rules That Govern Monotone Phenomena
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Some Recent Developments of Using Logical Analysis for Inferring a Boolean Function with Few Clauses
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A SAT-Based Approach to Learn Explainable Decision Sets
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Bicriteria programming with several modern applications
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A Greedy Randomized Adaptive Search Procedure (GRASP) for inferring logical clauses from examples in polynomial time and some extensions
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Learning logic functions from examples-better conceptions and models
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Integrated Model of Inductive-Deductive Inference Based on Finite Predicates and Implicative Regularities
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