Neural networks for proof-pattern recognition
Research output: Chapter in Book/Report/Conference proceeding › Other chapter contribution
- Ekaterina Komendantskaya
- Kacper Lichota
| Original language | English |
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| Title | Artificial Neural Networks and Machine Learning |
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| Subtitle | ICANN 2012 |
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| Editors | Alessandro E. P. Villa, Wlodzislaw Duch, Peter Erdi, Francesco Masulli, Gunther Palm |
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| Place of publication | Heidelberg |
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| Publisher | Springer |
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| Publication date | 2012 |
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| Pages | 427-434 |
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| Number of pages | 8 |
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| Volume | 7553 |
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| ISBN (Electronic) | 9783642332661 |
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| ISBN (Print) | 9783642332654 |
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| DOIs | |
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| State | Published |
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| Name | Lecture Notes in Computer Science |
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| Publisher | Springer |
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| Volume | 7553 |
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| ISSN (Print) | 0302-9743 |
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| ISSN (Electronic) | 1611-3349 |
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| Conference | 22nd International Conference on Artificial Neural Networks |
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| Country | Switzerland |
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| City | Lausanne |
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| Period | 11/09/12 → 14/09/12 |
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| Other | |
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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.