Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 7 of 7 for “"Evolving Data Streams"”.
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A Reservoir of Adaptive Algorithms for Online Learning from Evolving Data Streams
… change and development are essential aspects of evolving environments and applications, including, but not limited to, smart cities, military, medicine, nuclear reactors, self-driving cars, aviation, and aerospace. That is, the fundamental characteristics of such environments may evolve, and so …
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Advanced adaptive classifier methods for data streams
… has resulted in an overwhelming influx of big data. However, traditional batch learning models face significant obstacles in effectively learning from these vast and constantly evolving data streams and generating up-to-date outcomes. To overcome these limitations, Stream Learning (SL) has …
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Developing Learning Methods for Non-stationary and Imbalanced Data Streams
… of systems to both generate and collect data from a variety of sources. There is an increasing number of Internet of Things devices generating continuous data streams rapidly. Mining these data streams brings new opportunities but also introduces new challenges. Learning from these data …
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Improving ensembles and prediction intervals for machine learning on data streams
The rapid growth of streaming data presents significant challenges for traditional machine learning, including popular tasks like regression and classification. This thesis proposes adaptive and dynamic methods to address key issues, including concept drift, uncertainty quantification, and ensemble …
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Incremental Learning in Regression Contexts
… which models are updated with each new incoming data sample. In the real world, these models are usually deployed on Data Streams, that are potentially infinite in size and therefore cannot be tackled by common Batch or Offline Learning approaches. Other reasons for using incremental algorithms …
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New probabilistic approaches for detecting and evaluating concept drift in data streams
… forecasting, and real-time fraud detection, data distributions frequently shift, causing predictive models trained on historical data to underperform. This phenomenon, known as Concept Drift (CD), presents a major challenge in adaptive learning environments, necessitating ongoing monitoring …