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» Evaluating algorithms that learn from data streams
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76
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ICPR
2006
IEEE
15 years 11 months ago
Online Learning of Discriminative Patterns from Unlimited Sequences of Candidates
Recent research in object recognition has demonstrated the advantages of representing objects and scenes through localized patterns such as small image templates. In this paper we...
Ilkka Autio, Jussi T. Lindgren
80
Voted
ADBIS
2001
Springer
114views Database» more  ADBIS 2001»
15 years 2 months ago
Evaluation of Join Strategies for Distributed Mediation
Three join algorithms are evaluated in an environment with distributed main-memory based mediators and data sources. A streamed ship-out join ships bulks of tuples to a mediator ne...
Vanja Josifovski, Timour Katchaounov, Tore Risch
96
Voted
ICML
2005
IEEE
15 years 11 months ago
A martingale framework for concept change detection in time-varying data streams
In a data streaming setting, data points are observed one by one. The concepts to be learned from the data points may change infinitely often as the data is streaming. In this pap...
Shen-Shyang Ho
96
Voted
ASC
2011
14 years 5 months ago
Handling drifts and shifts in on-line data streams with evolving fuzzy systems
In this paper, we present new approaches to handling drift and shift in on-line data streams with the help of evolving fuzzy systems (EFS), which are characterized by the fact tha...
Edwin Lughofer, Plamen P. Angelov
CIKM
2009
Springer
15 years 4 months ago
Learning to rank from Bayesian decision inference
Ranking is a key problem in many information retrieval (IR) applications, such as document retrieval and collaborative filtering. In this paper, we address the issue of learning ...
Jen-Wei Kuo, Pu-Jen Cheng, Hsin-Min Wang