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» Budgeted Nonparametric Learning from Data Streams
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CIKM
2010
Springer
14 years 10 months ago
Partial drift detection using a rule induction framework
The major challenge in mining data streams is the issue of concept drift, the tendency of the underlying data generation process to change over time. In this paper, we propose a g...
Damon Sotoudeh, Aijun An
AAAI
2004
15 years 1 months ago
A Qualitative-Quantitative Methods-Based e-Learning Support System in Economic Education
This paper describes a new qualitative-quantitative simulator to help buyers learn how to make decisions when they purchase goods. In this paper, we propose an elearning support s...
Tokuro Matsuo, Takayuki Ito, Toramatsu Shintani
CIKM
2011
Springer
13 years 11 months ago
Emerging topic detection using dictionary learning
Streaming user-generated content in the form of blogs, microblogs, forums, and multimedia sharing sites, provides a rich source of data from which invaluable information and insig...
Shiva Prasad Kasiviswanathan, Prem Melville, Arind...
DCOSS
2007
Springer
15 years 5 months ago
Separating the Wheat from the Chaff: Practical Anomaly Detection Schemes in Ecological Applications of Distributed Sensor Networ
Abstract. We develop a practical, distributed algorithm to detect events, identify measurement errors, and infer missing readings in ecological applications of wireless sensor netw...
Luís M. A. Bettencourt, Aric A. Hagberg, Le...
KDD
2002
ACM
171views Data Mining» more  KDD 2002»
16 years 4 days ago
Mining complex models from arbitrarily large databases in constant time
In this paper we propose a scaling-up method that is applicable to essentially any induction algorithm based on discrete search. The result of applying the method to an algorithm ...
Geoff Hulten, Pedro Domingos