EPIA
2003
Springer
14 years 5 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...
ICDM
2003
IEEE
14 years 5 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...
PAKDD
2004
ACM
14 years 5 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...
ADC
2006
Springer
14 years 6 months ago
2006
Springer
Collaborative filtering is regarded as one of the most promising recommendation algorithms. Traditional approaches for collaborative filtering do not take concept drift into acc...
ICCBR
2007
Springer
14 years 6 months ago
2007
Springer
In this paper, we compare case-based spam filters, focusing on their resilience to concept drift. In particular, we evaluate how to track concept drift using a case-based spam fi...
ECML
2007
Springer
14 years 6 months ago
2007
Springer
In this paper we investigate the relation between transfer learning in reinforcement learning with function approximation and supervised learning with concept drift. We present a n...
MSR
2009
ACM
14 years 6 months ago
2009
ACM
Defect prediction is an important task in the mining of software repositories, but the quality of predictions varies strongly within and across software projects. In this paper we...
KDD
2007
ACM
15 years 21 days 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...
KDD
2009
ACM
15 years 25 days ago
2009
ACM
Advanced analysis of data streams is quickly becoming a key area of data mining research as the number of applications demanding such processing increases. Online mining when such...