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|Datum||Fr 18. Januar 2019, 11:30 Uhr|
|Raum||Raum 348 (Gebäude 50.34)|
|Vorheriger Termin||Fr 11. Januar 2019|
|Nächster Termin||Fr 25. Januar 2019|
|Titel||Density-Based Outlier Detection Benchmark on Synthetic Data|
|Kurzfassung||Outlier detection algorithms are widely used in application fields such as image processing and fraud detection. Thus, during the past years, many different outlier detection algorithms were developed. While a lot of work has been put into comparing the efficiency of these algorithms, comparing methods in terms of effectiveness is rather difficult. One reason for that is the lack of commonly agreed-upon benchmark data.
In this thesis the effectiveness of density-based outlier detection algorithms (such as KNN, LOF and related methods) on entirely synthetically generated data are compared, using its underlying density as ground truth.
|Titel||Dynamic adaptation to service usage policies|
|Kurzfassung||Developing and approach for dynamic adaptation to service usage policies.|
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