Exploring Classification Concept Drift on a Large News Text Corpus

Author:

Šilić Artur,Dalbelo Bašić Bojana

Publisher

Springer Berlin Heidelberg

Reference19 articles.

1. Fan, R., Chang, K., Hsieh, C., Wang, X., Lin, C.: LIBLINEAR: A library for large linear classification. J. Mach. Learn. Res. 9, 1871–1874 (2008)

2. Forman, G.: Tackling concept drift by temporal inductive transfer. Technical Report HPL-2006-20R1, Hewlett Packard Laboratories (2006)

3. Katakis, I., Tsoumakas, G., Banos, E., Bassiliades, N., Vlahavas, I.P.: An adaptive personalized news dissemination system. J. Intell. Inf. Syst. 32(2), 191–212 (2009)

4. Klinkenberg, R.: Learning drifting concepts: Example selection vs. example weighting. Intell. Data Anal. 8(3), 281–300 (2004)

5. Klinkenberg, R., Rüping, S.: Concept drift and the importance of examples. In: Text Mining – Theoretical Aspects and Applications, pp. 55–78. Physica-Verlag (2003)

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