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Concept drift

In predictive analytics and machine learning, the concept drift means that the statistical properties of the target variable, which the model is… Expand
Wikipedia

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
Review
2014
Review
2014
Concept drift primarily refers to an online supervised learning scenario when the relation between the input data and the target… Expand
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Highly Cited
2010
Highly Cited
2010
Online learning algorithms often have to operate in the presence of concept drift (i.e., the concepts to be learned can change… Expand
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Highly Cited
2007
Highly Cited
2007
We present an ensemble method for concept drift that dynamically creates and removes weighted experts in response to changes in… Expand
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Highly Cited
2005
Highly Cited
2005
On-line learning in domains where the target concept depends on some hidden context poses serious problems. A changing context… Expand
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Highly Cited
2005
Highly Cited
2005
An emerging problem in Data Streams is the detection of concept drift. This problem is aggravated when the drift is gradual over… Expand
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Review
2004
Review
2004
Alexey Tsymbal Department of Computer Science Trinity College Dublin, Ireland tsymbalo@tcd.ie April 29, 2004 Abstract In the real… Expand
Highly Cited
2004
Highly Cited
2004
On-line learning in domains where the target concept depends on some hidden context poses serious problems. A changing context… Expand
Highly Cited
2004
Highly Cited
2004
Most of the work in machine learning assume that examples are generated at random according to some stationary probability… Expand
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Highly Cited
2003
Highly Cited
2003
Algorithms for tracking concept drift are important for many applications. We present a general method based on the weighted… Expand
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Highly Cited
2000
Highly Cited
2000
For many learning tasks where data is collected over an extended period of time, its underlying distribution is likely to change… Expand
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