A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2018; you can also visit the original URL.
The file type is
Lecture Notes in Computer Science
A data stream is an ordered sequence of training instances arriving at a rate that does not permit to permanently store them in memory and leads to the necessity of online learning methods when trying to predict some hidden target variable. In addition, concept drift often occurs, what means that the statistical properties of the target variable may change over time. In this paper, we present a framework of solving the online pattern recognition problem in data streams under concept drift. Thedoi:10.1007/978-3-642-45062-4_26 fatcat:3mehx2mhevfzphl764wuxx3uja