TY - GEN
T1 - Personalised fading for stream data
AU - Veloso, Bruno
AU - Malheiro, Benedita
AU - Burguillo, Juan Carlos
AU - Foss, Jeremy
N1 - Funding Information:
This work was partially financed by the European Regional Development Fund (ERDF) through the Operational Programme for Competitiveness and Internationalisation (COMPETE Programme), within project ?FCOMP-01-0202-FEDER023151? and project ?POCI-01-0145-FEDER-006961?, and by national funds through the Funda??o para a Ci?ncia e Tecnologia (FCT)-Portuguese Foundation for Science and Technology-as part of project UID/EEA/50014/2013.3
Publisher Copyright:
Copyright 2017 ACM.
PY - 2017/4/3
Y1 - 2017/4/3
KW - Fading strategies
KW - Forgetting technique
KW - Stream mining
UR - http://www.scopus.com/inward/record.url?scp=85020940358&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85020940358&partnerID=8YFLogxK
U2 - 10.1145/3019612.3019868
DO - 10.1145/3019612.3019868
M3 - Conference contribution
AN - SCOPUS:85020940358
T3 - Proceedings of the ACM Symposium on Applied Computing
SP - 870
EP - 872
BT - 32nd Annual ACM Symposium on Applied Computing, SAC 2017
PB - Association for Computing Machinery
T2 - 32nd Annual ACM Symposium on Applied Computing, SAC 2017
Y2 - 4 April 2017 through 6 April 2017
ER -