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• W3042952112 abstract "In this work we address the problem of anomaly detection in large astronomical databases by machine learning methods. The importance of such study is justified by the existence of a large amount of astronomical data that can not be processed only by human resource. We evaluated five anomaly detection algorithms to find anomalies in the light curve data of the Open Supernova Catalog. Comparison of the algorithms revealed that expert supervised active anomaly detection method shows the best performance, while among purely unsupervised techniques Gaussian mixture model and one-class support vector machine methods outperform isolation forest and local outlier factor methods." @default.
• W3042952112 created "2020-07-23" @default.
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• W3042952112 date "2020-01-01" @default.
• W3042952112 modified "2023-09-28" @default.
• W3042952112 title "Realization of Different Techniques for Anomaly Detection in Astronomical Databases" @default.
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• W3042952112 doi "https://doi.org/10.1007/978-3-030-51913-1_7" @default.
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