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Timeline of machine learning

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| 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>
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| 2000 || || || {{w|LogitBoost}}, a [[w:Boosting (meta-algorithm)|boosting]] algorithm In {{w|machine learning}} and {{w|computational learning theory}}, is formulated by {{w|Jerome H. Friedman}}, {{w|Trevor Hastie}}, and {{w|Robert Tibshirani}}.<ref>{{cite journal |first1=Jerome |last1=Friedman |first2=Trevor |last2=Hastie |first3=Robert |last3=Tibshirani |title=Additive logistic regression: a statistical view of boosting |journal=Annals of Statistics |volume=28 |issue=2 |year=2000 |pages=337–407 |citeseerx=10.1.1.51.9525 |doi=10.1214/aos/1016218223}}</ref>
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| 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"/>
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