A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Spam Filtering
2014
Proceedings of the 4th International Conference on Web Intelligence, Mining and Semantics (WIMS14) - WIMS '14
Preserved Fulltext
Unsupervised learning, based on local structure Create groups of highly correlated data-points No re-clustering of data Our contribution : Active Learning combined with Incremental Clustering Use only 2% of the overall message labels Consider natural grouping of data : select representative instances for training
doi:10.1145/2611040.2611059
dblp:conf/wims/GeorgalaKP14
fatcat:qcet2r7jazgdtddp7qvrwa5acy