Detecting Outlying Time-Series with Global Alignment Kernels: Unterschied zwischen den Versionen
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|betreuer=Florian Kalinke | |betreuer=Florian Kalinke | ||
|termin=Institutsseminar/2020-12-11 | |termin=Institutsseminar/2020-12-11 | ||
|kurzfassung=Using outlier detection algorithms e.g., SVDD, for detecting outlying | |kurzfassung=Using outlier detection algorithms, e.g., Support Vector Data Description (SVDD), for detecting outlying time-series usually requires extracting domain-specific attributes. However, this indirect way needs expert knowledge, making SVDD impractical for many real-world use cases. Incorporating “Global Alignment Kernels” directly into SVDD to compute the distance between time-series data bypasses the attribute-extraction step and makes the application of SVDD independent of the underlying domain. | ||
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Version vom 2. Dezember 2020, 14:17 Uhr
Vortragende(r) | Haiko Thiessen | |
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Vortragstyp | Proposal | |
Betreuer(in) | Florian Kalinke | |
Termin | Fr 11. Dezember 2020 | |
Vortragsmodus | ||
Kurzfassung | Using outlier detection algorithms, e.g., Support Vector Data Description (SVDD), for detecting outlying time-series usually requires extracting domain-specific attributes. However, this indirect way needs expert knowledge, making SVDD impractical for many real-world use cases. Incorporating “Global Alignment Kernels” directly into SVDD to compute the distance between time-series data bypasses the attribute-extraction step and makes the application of SVDD independent of the underlying domain. |