1. A device comprising: a computer programmed to perform a machine learning method operating on training instances from a plurality of domains including one or more source domains and a target domain, with each training instance represented by values for a set of features wherein each domain corresponds to a traffic light surveillance camera and the training instances of each domain are images acquired by the corresponding surveillance camera of vehicles and the traffic light monitored by the corresponding surveillance camera, the set of labels indicate whether the imaged vehicle is running a red light, and the classifier outputs label predictions as to whether an imaged vehicle is running the traffic light, the machine learning method including the operations of: performing domain adaptation using stacked marginalized denoising autoencoding (mSDA) operating on the training instances to generate a stack of domain adaptation transform layers wherein each iteration of the domain adaptation includes corrupting the training instances in accord with feature corruption probability vectors for the features that are non-uniform over the domains including: corrupting the training instances from each source domain in accord with a feature corruption probability specific to that source domain; and corrupting the training instances from the target domain in accord with a feature corruption probability specific to the target domain; learning a classifier on the training instances transformed using the stack of domain adaptation transform layers; and generating a label prediction for an input instance from the target domain represented by values for the set of features by applying the classifier to the input instance transformed using the stack of domain adaptation transform domains.