An incremental learning algorithm for constructing Boolean functions from positive and negative examples

DOI10.1016/S0305-0548(01)00050-8zbMath1259.68100OpenAlexW2017317082MaRDI QIDQ1941951

T. Warren Liao, Evangelos Triantaphyllou, Jian-Hua Chen, Salvador Nieto Sánchez

Publication date: 25 March 2013

Published in: Computers \& Operations Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/s0305-0548(01)00050-8

zbMATH Keywords

Boolean functionsdata miningmachine learninglearning from examplesDNF and CNF formincremental and non-incremental learningthe one clause at a time (OCAT) approach for inferring a Boolean function

Mathematics Subject Classification ID

Computational learning theory (68Q32) Learning and adaptive systems in artificial intelligence (68T05)

Related Items (3)

Identifying the interacting positions of a protein using Boolean learning and support vector machines ⋮ Logical analysis of data as a tool for the analysis of probabilistic discrete choice behavior ⋮ Classification through incremental max-min separability

Uses Software

• Turbo Pascal

Cites Work

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• A greedy randomized adaptive search procedure (GRASP) for inferring logical clauses from examples in polynomial time and some extensions
• Generating logical expressions from positive and negative examples via a branch-and-bound approach
• Inference of a minimum size Boolean function from examples by using a new efficient branch-and-bound approach
• An approach to guided learning of Boolean functions
• A theory of the learnable
• A Relationship Between CNF and DNF Systems Derivable from Examples
• A Learning Network Using Adaptive Threshold Elements
• A formal system for information retrieval from files

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