Sciweavers

EPIA
2003
Springer
14 years 24 days ago
Adaptation to Drifting Concepts
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...
Gladys Castillo, João Gama, Pedro Medas
ICDM
2003
IEEE
181views Data Mining» more  ICDM 2003»
14 years 26 days ago
Dynamic Weighted Majority: A New Ensemble Method for Tracking Concept Drift
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...
Jeremy Z. Kolter, Marcus A. Maloof
PAKDD
2004
ACM
137views Data Mining» more  PAKDD 2004»
14 years 28 days ago
Fast and Light Boosting for Adaptive Mining of Data Streams
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...
Fang Chu, Carlo Zaniolo
ADC
2006
Springer
123views Database» more  ADC 2006»
14 years 1 months ago
Recency-based collaborative filtering
Collaborative filtering is regarded as one of the most promising recommendation algorithms. Traditional approaches for collaborative filtering do not take concept drift into acc...
Yi Ding, Xue Li, Maria E. Orlowska
ICCBR
2007
Springer
14 years 1 months ago
Catching the Drift: Using Feature-Free Case-Based Reasoning for Spam Filtering
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...
Sarah Jane Delany, Derek G. Bridge
ECML
2007
Springer
14 years 1 months ago
Transfer Learning in Reinforcement Learning Problems Through Partial Policy Recycling
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...
Jan Ramon, Kurt Driessens, Tom Croonenborghs
MSR
2009
ACM
14 years 2 months ago
Tracking concept drift of software projects using defect prediction quality
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...
Jayalath Ekanayake, Jonas Tappolet, Harald Gall, A...
KDD
2007
ACM
178views Data Mining» more  KDD 2007»
14 years 8 months ago
Real-time ranking with concept drift using expert advice
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...
Hila Becker, Marta Arias
KDD
2009
ACM
187views Data Mining» more  KDD 2009»
14 years 8 months ago
New ensemble methods for evolving data streams
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...
Albert Bifet, Bernhard Pfahringer, Geoffrey Holmes...