4 search results - page 1 / 1 » Using additive expert ensembles to cope with concept drift |
ICML
2005
IEEE
14 years 5 months ago
2005
IEEE
We consider online learning where the target concept can change over time. Previous work on expert prediction algorithms has bounded the worst-case performance on any subsequence ...
ICDM
2003
IEEE
13 years 10 months ago
2003
IEEE
Algorithms for tracking concept drift are important for many applications. We present a general method based on the Weighted Majority algorithm for using any online learner for co...
KDD
2007
ACM
14 years 5 months ago
2007
ACM
In many practical applications, one is interested in generating a ranked list of items using information mined from continuous streams of data. For example, in the context of comp...
ICDM
2008
IEEE
13 years 11 months ago
2008
IEEE
To cope with concept drift, we paired a stable online learner with a reactive one. A stable learner predicts based on all of its experience, whereas a reactive learner predicts ba...