Eine empirische Studie zur Verifikation von Unterschriften und zur Indikation von Fälschungen
DOI:
https://doi.org/10.17713/ajs.v42i2.159Abstract
Eine zufällige Stichprobe von Unterschriften, die auf mobilen Geräten mit Touch-Screen Display erzeugt wurde, soll hinsichtlich ihrer Verifikation untersucht werden. Zu diesem Zweck wurden verschiedene
Klassifizierungsalgorithmen verwendet und miteinander verglichen. Ein weiterer Datensatz wurde erfolgreich auf Indikation der Fälschungen untersucht. Zudem wird in diesem Beitrag eine Transformation der ursprünglichen Variablen beschrieben, die zur Erhöhung der Qualität sowohl der Verifikation,
als auch der Indikation der Fälschung beigetragen hat.
References
Aha, D. W., Kibler, D. and Albert, M. K. (1991). Instance-based learning algorithms. Machine Learning, 6, 37-66.
Cleary, J. G. and Trigg, L. E. (1995). An instance-based learner using an entropic distance measure. In 12th International Conference on Machine Learning (S. 108-114).
Conde, C., Ruiz, A. and Cabello, E. (2003). PCA vs low resolution images in face verification. In Proceedings of the 12th International Conference on Image Analysis and Processing (ICIAP’03).
Cost, S. and Salzberg, S. (1993). A weighted nearest neighbour algorithm for learning with symbolic features. Machine Learning, 10, 57-78.
Gehrke, M., Steinke, K. and Dzido, R. (2009). Writer recognition by characters, words and sentences. IEEE International Carnahan Conference on Security Technology, 281-288.
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P. and Witten, I. H. (2009). The WEKA Data Mining Software: An Update [Software-Handbuch]. (Volume 11,Issue 1)
Holte, R. C. (1993). Very simple classification rules perform well on most commonly used datasets. Machine Learning, 11, 63-91.
Linke, A. (2003). Spam oder nicht Spam? E-Mail sortieren mit Bayes Filtern. c’t, 17, 150-153.
Mardia, K. V., Kent, J. T. and Bibby, J. M. (1979). Multivariate Analysis. London, New York: Academic Press.
Ming-Yen, T. and Leu-Shing, L. (2005). Online writer identification using the point distribution model. IEEE International Conference on Systems, Man and Cybernetics, 1264-1268.
Santana, O., Travieso, C. M., Alonso, J. B. and Ferrer, M. A. (2010). Writer identification based on graphology techniques. IEEE Aerospace and Electronic System Magazine, 35-42.
Sesa-Nogueras, E. (2011). Discriminative power of online handwritten words for writer recognition. IEEE International Carnahan Conference on Security Technology (ICCST), 1-8.
Wang, D., Zhu, B. and Nakagawa, M. (2011). A digitale ink recognition server for handwritten Japanese text. IEEE International Conference on Document Analysis and Recognition, 146-150.
Downloads
Published
Issue
Section
License
The Austrian Journal of Statistics publish open access articles under the terms of the Creative Commons Attribution (CC BY) License.
The Creative Commons Attribution License (CC-BY) allows users to copy, distribute and transmit an article, adapt the article and make commercial use of the article. The CC BY license permits commercial and non-commercial re-use of an open access article, as long as the author is properly attributed.
Copyright on any research article published by the Austrian Journal of Statistics is retained by the author(s). Authors grant the Austrian Journal of Statistics a license to publish the article and identify itself as the original publisher. Authors also grant any third party the right to use the article freely as long as its original authors, citation details and publisher are identified.
Manuscripts should be unpublished and not be under consideration for publication elsewhere. By submitting an article, the author(s) certify that the article is their original work, that they have the right to submit the article for publication, and that they can grant the above license.
How to Cite
