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Learning algorithms for grammars of variable arity trees
Kamala Krithivasan
Published in
2007
Pages: 98 - 103
Abstract
Grammatical Inference is the technique by which a grammar that best describes a given set of input samples is inferred. This paper considers the inference of tree grammars from a set of sample input trees. Inference of grammars for fixed arity trees is well studied, in this paper we extend the method to give algorithms for inference of grammars for variable arity trees. We give algorithms for inference of local, single type and regular grammar and also consider the use of negative samples. The variable arity trees we consider can be used for representation of XML documents and the algorithms we have given can be used for validation as well as for schema inference. © 2007 IEEE.
About the journal
JournalProceedings - 6th International Conference on Machine Learning and Applications, ICMLA 2007
Open AccessYes
Concepts (16)
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    GRAMMATICAL INFERENCE (GI)
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    INPUT SAMPLES
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    International conferences
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    Machine-learning
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    REGULAR GRAMMAR
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    SCHEMA INFERENCE
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    TREE GRAMMARS
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    XML DOCUMENTS
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    Artificial intelligence
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    Education
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    Inference engines
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    Learning systems
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    Markup languages
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    Robot learning
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    Set theory
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    Learning algorithms