A MINSAT Approach for Learning in Logic Domains
| Autor: | Klaus Truemper, Giovanni Felici |
|---|---|
| Rok vydání: | 2002 |
| Předmět: |
Horn clause
Theoretical computer science Logic Programming Supervised learning General Engineering Multimodal logic data mining Satisfiability Description logic Probabilistic logic network Computer Science::Logic in Computer Science MINSAT Supervised Learning Inductive Inference Logic programming Logic optimization Mathematics |
| Zdroj: | INFORMS journal on computing 14 (2002): 20–36. info:cnr-pdr/source/autori:Felici, G.; Truemper, K./titolo:A MINSAT Approach for Learning in Logic Domains/doi:/rivista:INFORMS journal on computing/anno:2002/pagina_da:20/pagina_a:36/intervallo_pagine:20–36/volume:14 |
| ISSN: | 1526-5528 1091-9856 |
| DOI: | 10.1287/ijoc.14.1.20.7709 |
| Popis: | This paper describes a method for learning logic relationships that correctly classify a given data set. The method derives from given logic data certain minimum cost satisfiability problems, solves these problems, and deduces from the solutions the desired logic relationships. Uses of the method include data mining, learning logic in expert systems, and identification of critical characteristics for recognition systems. Computational tests have proved that the method is fast and effective. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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