Neural Networks for Proof-Pattern Recognition

Ekaterina Komendantskaya, Kacper Lichota

Research output: Chapter in Book/Report/Conference proceedingChapter

1 Citation (Scopus)

Abstract

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, Włodzisław Duch, Péter Érdi, Francesco Masulli, Günther Palm
PublisherSpringer
Pages427-434
Number of pages8
ISBN (Electronic)9783642332661
ISBN (Print)9783642332654
DOIs
Publication statusPublished - 2012

Publication series

NameLecture Notes in Computer Science
PublisherSpringer Berlin Heidelberg
Volume7553
ISSN (Print)0302-9743

Keywords

  • Machine learning
  • pattern-recognition
  • data mining
  • neural networks
  • first-order logic programs
  • automated proofs

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