text_combined: dash denver h isnzinjeyyiintelcom call papers machine learning algorithms event detection special issue machine learning journal submission deadline extended december 15 2007 httpwwwpittsburghintelresearchnetdhdashmlj_eventdetectionhtml denver dash dragos margineantu wengkeen wong guest editors would like invite submissions special issue machine learning journal machine learning algorithms event detection event detection task monitoring data source detecting occurrence event captured within source several sources complexity recent applications event detection problems variety data sources exploding encompassing multivariate records images video audio spatiotemporal data text documents unstructured data relational data volume data enormous often measured terabytes applications often involve monitoring human life critical assets thus require extreme timeliness high truepositive rates low falsepositive rates event may localized distributed time andor space event may neverbeforeseen dayzero event exist training data data source single sensor array identical sensors inhomogeneous mix various sensors problem often exacerbated presence active adversary complexities pose array challenges machine learning often standard paradigms supervised learning unsupervised learning even semisupervised active learning fit event detection problems well addressing issues would thus fill important gaps machine learning research would impact many pressing realworld applications studied today security public health biology environmental sciences manufacturing astrophysics finance business topics interest include limited event detection complex data video audio spatio temporal data text documents functional neuroimaging data relational data anomaly detection monitoring surveillance based sensor data multiple data sources integration learning domain knowledge event detection analysis capabilities learning algorithms event detection automated event detection safetycritical applications algorithms tools online event detection online limiting false alarm rates analysis error tradeoffsrisk models scaling detection algorithms large populations distributed algorithms monitoring surveillance validation testing event detection surveillance systems metrics performance dealing adversaries surveillance tasks machine learning research related novel application domains encourage prospective authors contact us email ckbijzwcomcastnet brief summary paper concept feedback especially survey papers papers focused applications submissions expected represent highquality significant contributions area machine learning algorithms andor applications machine learning application papers expected describe application detail present novel solutions general applicability beyond specific application authors follow standard formatting guidelines machine learning journal manuscripts administrative notes authors retain copyrights papers see publication agreement mlj website httppagessternnyuedufprovostmlj submissions reviewing handled electronically using standard procedures machine learning httpmachedmgrcom authors must register system submit manuscripts authors must select appropriate article type machine learning event detection submitting manuscripts accepted papers published electronically citable immediately print version appears schedule submission deadline december 15 2007 acceptance decisions march 10 2008 cameraready papers due may 5 2008 fri 08 aug 2008 020303 0800 uai final cfp deadline extended machine learning algorithms event detection