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Showing 1 to 4 of 4 for “"Data Stream Mining"”.
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Streaming Random Forests
Recent research addresses the problem of data-stream mining to deal with applications that require processing huge amounts of data such as sensor data analysis and financial applications. Data-stream mining algorithms incorporate special provisions to meet the requirements of stream-management …
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Learning Recurring Concepts from Data Streams in Ubiquitous Environments
… it is now possible to continuously record data at high speeds in a wide range of devices. The need to make sense of such massive amounts of data opens an opportunity to create new data stream classification techniques to model and predict the behavior of streaming data. When learning from …
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Unsupervised Concept Drift Detection in Data Streams
In data stream mining, efficiently detecting concept drifts is still challenging due to the high cost of collecting true class labels. Traditional detection methods usually need high computation and memory cost and is unable to distinguish between concept drift and novelty. To improve the drift …
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A Reservoir of Adaptive Algorithms for Online Learning from Evolving Data Streams
… algorithms due to the nature of incoming data as it continuously evolves. That is, the current efficient learning approach may become deprecated after a change in data or environment. Hence, the question 'how to have an efficient learning algorithm over time against evolving data?' has to …