Sciweavers

29 search results - page 2 / 6
» An Adaptive Distributed Ensemble Approach to Mine Concept-Dr...
Sort
View
PAKDD
2004
ACM
137views Data Mining» more  PAKDD 2004»
13 years 10 months 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
KDD
2007
ACM
178views Data Mining» more  KDD 2007»
14 years 5 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
ICDM
2010
IEEE
199views Data Mining» more  ICDM 2010»
13 years 2 months ago
Addressing Concept-Evolution in Concept-Drifting Data Streams
Abstract--The problem of data stream classification is challenging because of many practical aspects associated with efficient processing and temporal behavior of the stream. Two s...
Mohammad M. Masud, Qing Chen, Latifur Khan, Charu ...
SDM
2009
SIAM
191views Data Mining» more  SDM 2009»
14 years 1 months ago
Adaptive Concept Drift Detection.
An established method to detect concept drift in data streams is to perform statistical hypothesis testing on the multivariate data in the stream. Statistical decision theory off...
Anton Dries, Ulrich Rückert
DIS
2004
Springer
13 years 10 months ago
Mining Noisy Data Streams via a Discriminative Model
The two main challenges typically associated with mining data streams are concept drift and data contamination. To address these challenges, we seek learning techniques and models ...
Fang Chu, Yizhou Wang, Carlo Zaniolo