Record link: 
http://hdl.handle.net/20.500.12708/187877
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Title: 
A Relative Value Function Based Learning Beam Search for Longest Common Subsequence Problems
en
Citation: 
Huber, M., & Raidl, G. (2022). A Relative Value Function Based Learning Beam Search for Longest Common Subsequence Problems. In A. Quesada-Arencibia, J. C. Rodriguez, R. Moreno-Díaz, G. S. de Blasio, & C. R. Garcia (Eds.), Computer Aided Systems Theory - Extended Abstracts (pp. 22–23). IUCTC Universidad de Las Palmas de Gran Canaria.
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Publication Type: 
Inproceedings - Abstract Book Contribution
en
 
Konferenzbeitrag - Abstract Book Contribution
de
Language: 
English
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Authors: 
Huber, Marc 
Raidl, Günther 
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Organisational Unit: 
E192-01 - Forschungsbereich Algorithms and Complexity 
E192 - Institut für Logic and Computation 
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Published in: 
Computer Aided Systems Theory - Extended Abstracts 
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ISBN: 
978-84-09-38381-8
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Date (published): 
2022
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Event name: 
Eurocast 2022. 18th International Conference on Computer Aided Systems Theory 
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Event date: 
20-Feb-2022 - 25-Feb-2022
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Event place: 
Las Palmas de Gran Canaria, Spain
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Number of Pages: 
2
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Publisher: 
IUCTC Universidad de Las Palmas de Gran Canaria
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Keywords: 
Beam Search; Longest common subsequence problem; Machine Learning
en
Project title: 
Doktoratskolleg "Vienna Graduate School on Computational Optimization": W1260-N35 (Fonds zur Förderung der wissenschaftlichen Forschung (FWF)) 
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Research Areas: 
Logic and Computation: 100%
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Science Branch: 
1020 - Informatik: 80%
1010 - Mathematik: 20%
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Appears in Collections:
Conference Paper

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