MiniPalindrome
Paaßen B (2016)
Bielefeld University.
Datenpublikation
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MiniPalindrome.zip
376.08 KB
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Einrichtung
Abstract / Bemerkung
This is a dataset of 48 Java computer programs, all solving the same programming task, namely detecting whether all words in the input string are palindromes. The programs have been created by myself in 2012 as part of the DFG funded project _Learning Feedback for Dynamic Tutoring Systems_ (FIT) with grant number HA 2719/6-1. It is meant as a benchmark dataset for methods working on clustering and/or classification of structured data (sequences, trees or graphs). We provide the underlying raw data, as well as an intermediate graph representation and pre-calculated distance matrices.
Stichworte
Java programs
Erscheinungsjahr
2016
Copyright und Lizenzen
Page URI
https://pub.uni-bielefeld.de/record/2900666
Zitieren
Paaßen B. MiniPalindrome. Bielefeld University; 2016.
Paaßen, B. (2016). MiniPalindrome. Bielefeld University. doi:10.4119/unibi/2900666
Paaßen, Benjamin. 2016. MiniPalindrome. Bielefeld University.
Paaßen, B. (2016). MiniPalindrome. Bielefeld University.
Paaßen, B., 2016. MiniPalindrome, Bielefeld University.
B. Paaßen, MiniPalindrome, Bielefeld University, 2016.
Paaßen, B.: MiniPalindrome. Bielefeld University (2016).
Paaßen, Benjamin. MiniPalindrome. Bielefeld University, 2016.
Alle Dateien verfügbar unter der/den folgenden Lizenz(en):
Open Database License (ODbL) v1.0:
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MiniPalindrome.zip
376.08 KB
Access Level
Open Access
Zuletzt Hochgeladen
2019-09-25T06:45:04Z
MD5 Prüfsumme
d770c589499e93509d620f68f3f04d14
Material in PUB:
Wird zitiert von
Gaussian process prediction for time series of structured data
Paaßen B, Göpfert C, Hammer B (2016)
In: Proceedings of the ESANN, 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Verleysen M (Ed); Louvain-la-Neuve: Ciaco - i6doc.com: 41--46.
Paaßen B, Göpfert C, Hammer B (2016)
In: Proceedings of the ESANN, 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Verleysen M (Ed); Louvain-la-Neuve: Ciaco - i6doc.com: 41--46.
Wird zitiert von
Domain-Independent Proximity Measures in Intelligent Tutoring Systems
Mokbel B, Gross S, Paaßen B, Pinkwart N, Hammer B (2013)
In: Proceedings of the 6th International Conference on Educational Data Mining (EDM). D'Mello SK, Calvo RA, Olney A (Eds); 334-335.
Mokbel B, Gross S, Paaßen B, Pinkwart N, Hammer B (2013)
In: Proceedings of the 6th International Conference on Educational Data Mining (EDM). D'Mello SK, Calvo RA, Olney A (Eds); 334-335.
Wird zitiert von
Execution Traces as a Powerful Data Representation for Intelligent Tutoring Systems for Programming
Paaßen B, Jensen J, Hammer B (2016)
In: Proceedings of the 9th International Conference on Educational Data Mining. Barnes T, Chi M, Feng M (Eds); Raleigh, North Carolina, USA: International Educational Datamining Society: 183-190.
Paaßen B, Jensen J, Hammer B (2016)
In: Proceedings of the 9th International Conference on Educational Data Mining. Barnes T, Chi M, Feng M (Eds); Raleigh, North Carolina, USA: International Educational Datamining Society: 183-190.
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