{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,22]],"date-time":"2025-02-22T05:27:52Z","timestamp":1740202072056,"version":"3.37.3"},"reference-count":0,"publisher":"IOS Press","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2016]]},"abstract":"<jats:p>A new algorithm named EXPected Similarity Estimation (EXPOSE) was recently proposed to solve the problem of large-scale anomaly detection. It is a non-parametric and distribution free kernel method based on the Hilbert space embedding of probability measures. Given a dataset of n samples, EXPOSE takes &amp;Oscr;&amp;lpar;n&amp;rpar; time to build a model and &amp;Oscr;&amp;lpar;1&amp;rpar; time per prediction.<\/jats:p>","DOI":"10.3233\/978-1-61499-672-9-12","type":"book-chapter","created":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T10:27:24Z","timestamp":1740133644000},"source":"Crossref","is-referenced-by-count":0,"title":["Constant Time EXPected Similarity Estimation for Large-Scale Anomaly Detection"],"prefix":"10.3233","author":[{"family":"Schneider Markus","sequence":"additional","affiliation":[]},{"family":"Ertel Wolfgang","sequence":"additional","affiliation":[]},{"family":"Palm G&uuml;nther","sequence":"additional","affiliation":[]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2016"],"original-title":[],"deposited":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T11:16:58Z","timestamp":1740136618000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.medra.org\/servlet\/aliasResolver?alias=iospressISBN&isbn=978-1-61499-671-2&spage=12&doi=10.3233\/978-1-61499-672-9-12"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016]]},"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/978-1-61499-672-9-12","relation":{},"ISSN":["0922-6389"],"issn-type":[{"value":"0922-6389","type":"print"}],"subject":[],"published":{"date-parts":[[2016]]}}}