Efficient Quantum One-Class Support Vector Machines for Anomaly Detection Using Randomized Measurements and Variable Subsampling

Detalhes bibliográficos

Publicado no:	arXiv.org (Jul 30, 2024), p. n/a
Autor principal:	Kölle, Michael
Outros Autores:	Ahouzi, Afrae, Debus, Pascal, Çetiner, Elif, Müller, Robert, Schuman, Daniëlle, Linnhoff-Popien, Claudia
Publicado em:	Cornell University Library, arXiv.org
Assuntos:	Testing time Complexity Anomalies Support vector machines Kernel functions Variance Time measurement
Acesso em linha:	Citation/Abstract Full text outside of ProQuest
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