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» Knowledge Maintenance on Data Streams with Concept Drifting
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SDM
2007
SIAM
198views Data Mining» more  SDM 2007»
13 years 9 months ago
Learning from Time-Changing Data with Adaptive Windowing
We present a new approach for dealing with distribution change and concept drift when learning from data sequences that may vary with time. We use sliding windows whose size, inst...
Albert Bifet, Ricard Gavaldà
KDD
2006
ACM
129views Data Mining» more  KDD 2006»
14 years 8 months ago
Suppressing model overfitting in mining concept-drifting data streams
Mining data streams of changing class distributions is important for real-time business decision support. The stream classifier must evolve to reflect the current class distributi...
Haixun Wang, Jian Yin, Jian Pei, Philip S. Yu, Jef...
CORR
2004
Springer
122views Education» more  CORR 2004»
13 years 7 months ago
"In vivo" spam filtering: A challenge problem for data mining
Spam, also known as Unsolicited Commercial Email (UCE), is the bane of email communication. Many data mining researchers have addressed the problem of detecting spam, generally by...
Tom Fawcett
CIDR
2011
266views Algorithms» more  CIDR 2011»
12 years 11 months ago
Consistency in a Stream Warehouse
A stream warehouse is a Data Stream Management System (DSMS) that stores a very long history, e.g. years or decades; or equivalently a data warehouse that is continuously loaded. ...
Lukasz Golab, Theodore Johnson
TNN
2008
178views more  TNN 2008»
13 years 7 months ago
IMORL: Incremental Multiple-Object Recognition and Localization
This paper proposes an incremental multiple-object recognition and localization (IMORL) method. The objective of IMORL is to adaptively learn multiple interesting objects in an ima...
Haibo He, Sheng Chen