%0 Journal Article %1 englhardt2020efficient %A Englhardt, Adrian %A Trittenbach, Holger %A Kottke, Daniel %A Sick, Bernhard %A Böhm, Klemens %D 2020 %J arXiv e-prints %K - Computer Learning Learning, Machine Science Statistics %P arXiv:2009.13853 %T Efficient SVDD Sampling with Approximation Guarantees for the Decision Boundary

@article{englhardt2020efficient, added-at = {2022-01-07T10:37:59.000+0100}, archiveprefix = {arXiv}, author = {{Englhardt}, Adrian and {Trittenbach}, Holger and {Kottke}, Daniel and {Sick}, Bernhard and {B{\"o}hm}, Klemens}, biburl = {https://www.bibsonomy.org/bibtex/29e12863654ac1559327560cf57a726ba/ies}, eid = {arXiv:2009.13853}, eprint = {2009.13853}, interhash = {1b5d19b7646d4f4f3b45b0103e4b30d5}, intrahash = {9e12863654ac1559327560cf57a726ba}, journal = {arXiv e-prints}, keywords = {- Computer Learning Learning, Machine Science Statistics}, pages = {arXiv:2009.13853}, primaryclass = {cs.LG}, timestamp = {2022-01-07T10:37:59.000+0100}, title = {{Efficient SVDD Sampling with Approximation Guarantees for the Decision Boundary}}, year = 2020 }