VBB Midi Dataset
Paaßen B (2013)
Bielefeld University.
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Einrichtung
Abstract / Bemerkung
The VBB Midi dataset contains a distance matrix and labels for 352 train and metro stops in the Berlin public transportation net. The travel time between the stops is used as distance while their geographical position gives rise to their label (there are twelve labels, one for each of Berlins gouvernmental districts). The dataset is constructed for benchmark purposes on relational classification methods and especially prototype-based methods. The geographical two-dimensional position of each stop allows for an intuitive visualization and interpretation of stops and prototype positions.
The original timetable data is provided by the Verkehrsverbund Berlin Brandenburg (VBB) under the Creative Commons Attribution License:
Data: http://daten.berlin.de/datensaetze/vbb-fahrplan-2013
License: http://creativecommons.org/licenses/by/3.0/de/
Stichworte
relational LVQ prototype interpretable kernel VBB public transportation multiclass classification
Erscheinungsjahr
2013
Copyright und Lizenzen
Page URI
https://pub.uni-bielefeld.de/record/2692491
Zitieren
Paaßen B. VBB Midi Dataset. Bielefeld University; 2013.
Paaßen, B. (2013). VBB Midi Dataset. Bielefeld University. doi:10.4119/unibi/2692491
Paaßen, Benjamin. 2013. VBB Midi Dataset. Bielefeld University.
Paaßen, B. (2013). VBB Midi Dataset. Bielefeld University.
Paaßen, B., 2013. VBB Midi Dataset, Bielefeld University.
B. Paaßen, VBB Midi Dataset, Bielefeld University, 2013.
Paaßen, B.: VBB Midi Dataset. Bielefeld University (2013).
Paaßen, Benjamin. VBB Midi Dataset. Bielefeld University, 2013.
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Open Data Commons Attribution License (ODC-By) v1.0:
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Open Access
Zuletzt Hochgeladen
2019-09-25T06:35:54Z
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57e93035668f8b06b435330957c8c9fb
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Wird zitiert von
Learning interpretable kernelized prototype-based models
Hofmann D, Schleif F-M, Paaßen B, Hammer B (2014)
Neurocomputing 141: 84-96.
Hofmann D, Schleif F-M, Paaßen B, Hammer B (2014)
Neurocomputing 141: 84-96.