Browsing by Subject "String generalization"

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    Generalizing predicates with string arguments
    (Springer New York LLC, 2006-06) Cicekli, I.; Cicekli, N. K.
    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.
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    Induction of logical relations based on specific generalization of strings
    (2007) Uzun, Yasin
    Learning logical relations from examples expressed as first order facts has been studied extensively by the Inductive Logic Programming research. Learning with positive-only data may cause overgeneralization of examples leading to inconsistent resulting hypotheses. A learning heuristic inferring specific generalization of strings based on unique match sequences is shown to be capable of learning predicates with string arguments. This thesis outlines the effort showed to build an inductive learner based on the idea of specific generalization of strings that generalizes given clauses considering the background knowledge using least general generalization schema. The system is also extended to generalize predicates having numeric arguments and shown to be capable of learning concepts such as family relations, grammar learning and predicting mutagenecity using numeric data.