Neural networks for proof-pattern recognition

Ekaterina Komendantskaya, Kacper Lichota

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    1 Citation (Scopus)


    We propose a new method of feature extraction that allows to apply pattern-recognition abilities of neural networks to data-mine automated proofs. We propose a new algorithm to represent proofs for first-order logic programs as feature vectors; and present its implementation. We test the method on a number of problems and implementation scenarios, using three-layer neural nets with backpropagation learning.
    Original languageEnglish
    Title of host publicationArtificial Neural Networks and Machine Learning – ICANN 2012
    Subtitle of host publication22nd International Conference on Artificial Neural Networks, Lausanne, Switzerland, September 11-14, 2012, Proceedings, Part II
    EditorsAlessandro E. P. Villa, Wlodzislaw Duch, Peter Erdi, Francesco Masulli, Gunther Palm
    Place of PublicationBerlin
    Number of pages8
    ISBN (Electronic)9783642332661
    ISBN (Print)9783642332654
    Publication statusPublished - 2012
    Event22nd International Conference on Artificial Neural Networks - Building Internef of the UNIL Campus Dorigny, Lausanne, Switzerland
    Duration: 11 Sep 201214 Sep 2012

    Publication series

    NameLecture notes in computer science
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349


    Conference22nd International Conference on Artificial Neural Networks
    Abbreviated titleICANN 2012
    Internet address

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