62,434
edits
Changes
no edit summary
|-
| 1999 || || || "Computer-aided diagnosis catches more cancers. Computers can’t cure cancer (yet), but they can help us diagnose it. The CAD Prototype Intelligent Workstation, developed at the University of Chicago, reviewed 22,000 mammograms and detected cancer 52% more accurately than radiologists did."
|-
| 2000 || || Algorithm || In {{w|anomaly detection}}, the {{w|local outlier factor}} (LOF) is an algorithm proposed by Markus M. Breunig, {{w|Hans-Peter Kriegel}}, Raymond T. Ng and Jörg Sander for finding anomalous data points by measuring the local deviation of a given data point with respect to its neighbours.<ref>{{Cite conference| doi = 10.1145/335191.335388| title = LOF: Identifying Density-based Local Outliers| year = 2000| last1 = Breunig | first1 = M. M.| last2 = Kriegel | first2 = H.-P. | authorlink2 = Hans-Peter Kriegel| last3 = Ng | first3 = R. T.| last4 = Sander | first4 = J.| work = Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data| series = [[SIGMOD]]| isbn = 1-58113-217-4| pages = 93–104| url = http://www.dbs.ifi.lmu.de/Publikationen/Papers/LOF.pdf}}</ref>
|-
| 2001 || || || "Another ensemble model explored by Breiman [12] in 2001 that ensembles multiple decision trees where each of them is curated by a random subset of instances and each node is selected from a random subset of features."<ref name="erogol.comt"/>