Towards Efficient Quantum Anomaly Detection: One-Class SVMs using Variable Subsampling and Randomized Measurements

Detalles Bibliográficos

Publicado en:	arXiv.org (Dec 14, 2023), p. n/a
Autor Principal:	Kölle, Michael
Outros autores:	Ahouzi, Afrae, Debus, Pascal, Müller, Robert, Schuman, Danielle, Linnhoff-Popien, Claudia
Publicado:	Cornell University Library, arXiv.org
Materias:	Machine learning Quantum computing Radial basis function Complexity Anomalies Support vector machines Kernel functions Cognitive tasks
Acceso en liña:	Citation/Abstract Full text outside of ProQuest
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