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» New ensemble methods for evolving data streams
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ICONIP
1998
13 years 10 months ago
ECOS: Evolving Connectionist Systems and the ECO Learning Paradigm
The paper presents a framework called ECOS for Evolving COnnectionist Systems. ECOS evolve through incremental learning. They can accommodate any new input data, including new fea...
Nikola K. Kasabov
SIGMOD
2006
ACM
158views Database» more  SIGMOD 2006»
14 years 8 months ago
Continuous query processing in data streams using duality of data and queries
Recent data stream systems such as TelegraphCQ have employed the well-known property of duality between data and queries. In these systems, query processing methods are classified...
Hyo-Sang Lim, Jae-Gil Lee, Min-Jae Lee, Kyu-Young ...
ICDM
2008
IEEE
145views Data Mining» more  ICDM 2008»
14 years 3 months ago
Paired Learners for Concept Drift
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...
Stephen H. Bach, Marcus A. Maloof
KDD
2009
ACM
191views Data Mining» more  KDD 2009»
14 years 9 months ago
Efficient methods for topic model inference on streaming document collections
Topic models provide a powerful tool for analyzing large text collections by representing high dimensional data in a low dimensional subspace. Fitting a topic model given a set of...
Limin Yao, David M. Mimno, Andrew McCallum
SDM
2012
SIAM
452views Data Mining» more  SDM 2012»
11 years 11 months ago
Density-based Projected Clustering over High Dimensional Data Streams
Clustering of high dimensional data streams is an important problem in many application domains, a prominent example being network monitoring. Several approaches have been lately ...
Irene Ntoutsi, Arthur Zimek, Themis Palpanas, Peer...