A New Approach Adapting Neural Network Classifiers to Sudden Changes in Nonstationary Environments

dc.contributor.advisor	Nicholson, Charles
dc.contributor.author	Amidon, Alexandra
dc.contributor.committeeMember	Kang, Ziho
dc.contributor.committeeMember	Shehab, Randa
dc.date.accessioned	2017-05-11T20:46:29Z
dc.date.available	2017-05-11T20:46:29Z
dc.date.issued	2017-05-12
dc.date.manuscript	2017-05-09
dc.description.abstract	Business are increasingly analyzing streaming data in real time to achieve business objectives such as monetization or quality control. The predictive algorithms applied to streaming data sources are often trained sequentially by updating the model weights after each new data point arrives. When disruptions or changes in the data generating process occur, the online learning process allows the algorithm to slowly learn the changes; however, there may be a period of time after concept drift during which the predictive algorithm underperforms. This thesis introduces a method that makes online neural network classifiers more resilient to these concept drifts by utilizing data about concept drift to update neural network parameters.	en_US
dc.identifier.uri	http://hdl.handle.net/11244/50801
dc.language	en_US	en_US
dc.subject	concept drift	en_US
dc.subject	neural networks	en_US
dc.subject	data streams	en_US
dc.thesis.degree	Master of Science	en_US
dc.title	A New Approach Adapting Neural Network Classifiers to Sudden Changes in Nonstationary Environments	en_US
ou.group	Gallogly College of Engineering	en_US