Event Prediction: Learning from Ambiguous Examples

@misc{citeulike:236168, abstract = {Event prediction is an important problem with many realworld applications. For the majority of these applications, it is not necessary to predict the exact time an event will occur---it is acceptable to predict that the event will occur within some time interval. The use of this time interval introduces ambiguity into the event prediction problem, permitting it to be viewed as a multiple-instance learning problem. We have developed timeweaver, a genetic-algorithm based learning system that is...}, author = {Weiss, Gary M. and Hirsh, Haym}, citeulike-article-id = {236168}, keywords = {instance, learning, multiple}, posted-at = {2005-06-24 20:49:29}, priority = {2}, title = {Event Prediction: Learning from Ambiguous Examples}, url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.9.2133} }

TY - GEN ID - citeulike:236168 L3 - citeulike-article-id:236168 N2 - Event prediction is an important problem with many realworld applications. For the majority of these applications, it is not necessary to predict the exact time an event will occur---it is acceptable to predict that the event will occur within some time interval. The use of this time interval introduces ambiguity into the event prediction problem, permitting it to be viewed as a multiple-instance learning problem. We have developed timeweaver, a genetic-algorithm based learning system that is... TI - Event Prediction: Learning from Ambiguous Examples KW - instance KW - learning KW - multiple AU - Weiss, Gary AU - Hirsh, Haym UR - http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.9.2133 ER -