129 search results - page 2 / 26 » Adaptive Concept Drift Detection. |
ADMA
2006
Springer
13 years 9 months ago
2006
Springer
Abstract. Most of the work in Machine Learning assume that examples are generated at random according to some stationary probability distribution. In this work we study the problem...
PAKDD
2004
ACM
13 years 10 months ago
2004
ACM
Supporting continuous mining queries on data streams requires algorithms that (i) are fast, (ii) make light demands on memory resources, and (iii) are easily to adapt to concept dr...
KDD
1998
ACM
13 years 9 months ago
1998
ACM
The task in the computer security domain of anomaly detection is to characterize the behaviors of a computer user (the `valid', or `normal' user) so that unusual occurre...
EPIA
2003
Springer
13 years 10 months ago
2003
Springer
Most of supervised learning algorithms assume the stability of the target concept over time. Nevertheless in many real-user modeling systems, where the data is collected over an ex...
COMAD
2009
13 years 6 months ago
2009
Mining evolving data streams for concept drifts has gained importance in applications like customer behavior analysis, network intrusion detection, credit card fraud detection. Se...