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      Generalizing predicates with string arguments

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      Author(s)
      Cicekli, I.
      Cicekli, N. K.
      Date
      2006-06
      Source Title
      Applied Intelligence: the international journal of artificial intelligence, neural networks, and complex problem-solving technologies
      Print ISSN
      0924-669X
      Publisher
      Springer New York LLC
      Volume
      25
      Pages
      23 - 36
      Language
      English
      Type
      Article
      Item Usage Stats
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      206
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      Abstract
      The least general generalization (LGG) of strings may cause an over-generalization in the generalization process of the clauses of predicates with string arguments. We propose a specific generalization (SG) for strings to reduce over-generalization. SGs of strings are used in the generalization of a set of strings representing the arguments of a set of positive examples of a predicate with string arguments. In order to create a SG of two strings, first, a unique match sequence between these strings is found. A unique match sequence of two strings consists of similarities and differences to represent similar parts and differing parts between those strings. The differences in the unique match sequence are replaced to create a SG of those strings. In the generalization process, a coverage algorithm based on SGs of strings or learning heuristics based on match sequences are used. © Springer Science + Business Media, LLC 2006.
      Keywords
      Inductive logic programming
      Machine learning
      String generalization
      Algorithms
      Heuristic methods
      Learning systems
      Process control
      Artificial intelligence
      Permalink
      http://hdl.handle.net/11693/38112
      Published Version (Please cite this version)
      http://dx.doi.org/10.1007/s10489-006-8864-1
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      • Department of Computer Engineering 1561
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