7 search results - page 1 / 2 » Dynamic Weighted Majority: A New Ensemble Method for Trackin... |
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...
MCS
2009
Springer
13 years 9 months ago
2009
Springer
We have recently introduced an incremental learning algorithm, Learn++ .NSE, for Non-Stationary Environments, where the data distribution changes over time due to concept drift. Le...
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...
MCS
2007
Springer
13 years 11 months ago
2007
Springer
Abstract. We describe an ensemble of classifiers based algorithm for incremental learning in nonstationary environments. In this formulation, we assume that the learner is presente...
ICPR
2008
IEEE
14 years 6 months ago
2008
IEEE
We outline an incremental learning algorithm designed for nonstationary environments where the underlying data distribution changes over time. With each dataset drawn from a new e...