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 16 of 16 for “"evolving data"”.
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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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Robust and Data-Driven Uncertainty Quantification Methods as Real-Time Decision Support in Data-Driven Models
The growing complexity and data in modern engineering and physical systems require robust frameworks for real-time decision-making. Data-driven models trained on observational data enable faster predictions but face key challenges—data corruption, bias, limited interpretability, and uncertainty …
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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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A Service Late Binding Enabled Solution for Data Integration from Autonomous and Evolving Databases
Integrating data from autonomous, distributed and heterogeneous data sources to provide a unified vision is a common demand for many businesses. Since the data sources may evolve frequently to satisfy their own independent business needs, solutions which use hard coded queries to integrate …
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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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Geometric optimization algorithms for linear regression on fixed-rank matrices
Nowadays, large and rapidly evolving data sets are commonly encountered in many modern applications. Efficiently mining and exploiting these data sets generally results in the extraction of valuable information and therefore appears as an important challenge in various domains including network …
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Semi-automated reconstruction of biological networks based on a life science data warehouse
… years leads to a multiplicity of different databases and information systems. Typically, those data is available via the World Wide Web for further investigation. Usually, biological and life science data that describe different aspects of a biological system are distributed and spread over …
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Clustering and dimensionality reduction for time-series service monitoring data
… monitoring applications continuously produce data to monitor their availability, therefore, high dimensionality, unlabeled data and changing data distribution are all prevalent. In this thesis, we efficiently address these three issues using the constructed service monitoring dataset. Higher …
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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 …
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Towards Logical Reasoning and Learning in Open and Dynamic Environments
… and dynamic world where knowledge is constantly evolving, and Artificial Intelligence (AI) systems must adapt to newly added information. A crucial aspect of AI systems is performing robust logical reasoning that makes reliable inferences, generates hypotheses, and extracts meaningful insights …
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Active learning for data streams.
With the exponential growth of data amount and sources, access to large collections of data has become easier and cheaper. However, data is generally unlabelled and labels are often difficult, expensive, and time consuming to obtain. Two learning paradigms have been used by machine learning …
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Exploring the power of heterogeneous information sources
The big data challenge is one unique opportunity for both data mining and database research and engineering. A vast ocean of data are collected from trillions of connected devices in real time on a daily basis, and useful knowledge is usually buried in data of multiple genres, from different …
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The COMPASS Paradigm For The Systematic Evaluation Of U.S. Army Command And Control Systems Using Neural Network And Discrete Event Computer Simulation
… successive TOC observation events to generate an evolving data store that supports the two phases of the project. Phase I consists of the observation of heavy maneuver battalion and brigade TOCs during peacetime exercises. The term "heavy maneuver" is used to connotate main battle forces such as …