terran lane bidftbcsunmedu call papers contributions icml 2006 workshop machine learning algorithms surveillance event detection organizers denver dash intel research terran lane university new mexico dragos margineantu boeing wengkeen wong oregon state university workshop description common task many domains involves monitoring routinely collected data anomalous events typically inspection data performed surveillance purposes instance security guard needs examine video footage signs intrusion scientists also perform similar task analyzing experimental data detection anomalous events experimental data often leads new scientific discoveries order refer monitoring process general term possible call event detection event detection potential impact wide range important realworld applications ranging security finance public health medicine biology environmental science manufacturing astrophysics business economics recent past human beings laborious job manually examining collected data event detection however emergence computers massive world interconnectivity made easier collect data provided reasons simple forms data univariate time series effectively monitored using wellestablished techniques regression boxjenkins models methods statistical quality control data however become increasingly complex recent years multivariate records images video footage audio recordings spatial spatiotemporal data text documents even relational data routinely collected one might expect existing work machine learning would wellsuited task however practice peculiarities application often grossly violate standard assumptions machine learning often none standard paradigms supervised learning unsupervised learning even semisupervised active learning fit situation well result new algorithms need created order address issues fill important gap machine learning research would impact many pressing real world applications studied today presence event detection problems seems widespread throughout areas machine learning workshop provide forum participants many different communities share ideas experiences addition workshop benefit diversity icml attendees span entire spectrum applied theoretical research topics interest include limited event detection complex data video audio spatio temporal data text documents functional neuroimaging data relational data event detection based multiple data sources integration data mining components expert knowledge event detection analysis capabilities learning algorithms event detection automated event detection safetycritical applications algorithms tools online event detection limiting reducing false alarm rates analysis error tradeoffs cost models event detection validation testing event detection systems metrics performance failures applying data mining learning event detection dealing adversaries attempt remain undetected novel application domains workshop intended continuation workshop data mining methods anomaly detection kdd 2005 greater emphasis event detection richer data representations spatiotemporal data relational data unstructured data multimedia files workshop format workshop consist combination invited talks presentations papers discussion period presentation allocated 1020 minutes presentation 10 minutes discussion attendance workshop limited 4050 people participation submissions participate workshop please send email message weng keen wong oljteecsoregonstateedu giving name affiliation address email address brief description reasons wanting attend webpage workshop http webengroregonstateeduwongworkshopsicml2006 important dates april 28 2006 tentative workshop submissions due may 19 2006 tentative notification acceptance june 18 2006 workshop proceedings posted webpage june 29 2006 icml workshops terran lane sdsrpyhdwhxcsunmedu wwwhttpwwwcsunmeduterran gpg keyhttpwwwcsunmeduterrangpg_pub_keyshtml dont want go among mad people alice remarked oh cant help said cat mad im mad youre mad know im mad said alice must said cat wouldnt come wed 06 aug 2008 024536 0700 uai cfp workshop machine learning algorithms surveillance event detection icml 2006