**Neural networks for proof-pattern recognition.** / Komendantskaya, Ekaterina; Lichota, Kacper.

Research output: Chapter in Book/Report/Conference proceeding › Other chapter contribution

Komendantskaya, E & Lichota, K 2012, 'Neural networks for proof-pattern recognition'. in AEP Villa, W Duch, P Erdi, F Masulli & G Palm (eds), *Artificial Neural Networks and Machine Learning : ICANN 2012.* vol. 7553 , Lecture Notes in Computer Science, vol. 7553, Springer, Heidelberg, pp. 427-434, 22nd International Conference on Artificial Neural Networks, Lausanne, Switzerland, 11-14 September.

Komendantskaya, E., & Lichota, K. (2012). Neural networks for proof-pattern recognition. In Villa, A. E. P., Duch, W., Erdi, P., Masulli, F., & Palm, G. (Eds.), *Artificial Neural Networks and Machine Learning .* (pp. 427-434). (Lecture Notes in Computer Science). Heidelberg: Springer. doi: 10.1007/978-3-642-33266-1_53

Komendantskaya E, Lichota K. Neural networks for proof-pattern recognition. In Villa AEP, Duch W, Erdi P, Masulli F, Palm G, editors, Artificial Neural Networks and Machine Learning : ICANN 2012. Heidelberg: Springer. 2012. p. 427-434. (Lecture Notes in Computer Science).

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title = "Neural networks for proof-pattern recognition",

publisher = "Springer",

author = "Ekaterina Komendantskaya and Kacper Lichota",

year = "2012",

editor = "Villa, {Alessandro E. P.} and Wlodzislaw Duch and Peter Erdi and Francesco Masulli and Gunther Palm",

volume = "7553",

isbn = "9783642332654",

series = "Lecture Notes in Computer Science",

pages = "427-434",

booktitle = "Artificial Neural Networks and Machine Learning",

}

TY - CHAP

T1 - Neural networks for proof-pattern recognition

A1 - Komendantskaya,Ekaterina

A1 - Lichota,Kacper

AU - Komendantskaya,Ekaterina

AU - Lichota,Kacper

PB - Springer

CY - Heidelberg

PY - 2012

Y1 - 2012

N2 - 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. © 2012 Springer-Verlag.

AB - 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. © 2012 Springer-Verlag.

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U2 - 10.1007/978-3-642-33266-1_53

DO - 10.1007/978-3-642-33266-1_53

M1 - Other chapter contribution

SN - 9783642332654

VL - 7553

BT - Artificial Neural Networks and Machine Learning

T2 - Artificial Neural Networks and Machine Learning

A2 - Palm,Gunther

ED - Palm,Gunther

T3 - Lecture Notes in Computer Science

T3 - en_GB

SP - 427

EP - 434

ER -