Learning Algorithms for Domain Adaptation

Abstract

A fundamental assumption for any machine learning task is to have training and test data instances drawn from the same distribution while having a sufficiently large number of training instances. In many practical settings, this ideal assumption is invalidated as the labeled training instances are scarce and there is a high cost associated with labeling… (More)
DOI: 10.1007/978-3-642-05224-8_23

2 Figures and Tables

Topics

  • Presentations referencing similar topics