- search hit 1 of 1
Static prediction games for adversarial learning problems
- The standard assumption of identically distributed training and test data is violated when the test data are generated in response to the presence of a predictive model. This becomes apparent, for example, in the context of email spam filtering. Here, email service providers employ spam filters, and spam senders engineer campaign templates to achieve a high rate of successful deliveries despite the filters. We model the interaction between the learner and the data generator as a static game in which the cost functions of the learner and the data generator are not necessarily antagonistic. We identify conditions under which this prediction game has a unique Nash equilibrium and derive algorithms that find the equilibrial prediction model. We derive two instances, the Nash logistic regression and the Nash support vector machine, and empirically explore their properties in a case study on email spam filtering.
Author details: | Michael Brückner, Christian Kanzow, Tobias SchefferORCiD |
---|---|
ISSN: | 1532-4435 |
Title of parent work (English): | Journal of machine learning research |
Publisher: | Microtome Publishing |
Place of publishing: | Cambridge, Mass. |
Publication type: | Article |
Language: | English |
Year of first publication: | 2012 |
Publication year: | 2012 |
Release date: | 2017/03/26 |
Tag: | Nash equilibrium; adversarial classification; static prediction games |
Volume: | 13 |
Number of pages: | 38 |
First page: | 2617 |
Last Page: | 2654 |
Funding institution: | German Science Foundation (DFG) [SCHE540/12-1]; STRATO AG |
Organizational units: | Mathematisch-Naturwissenschaftliche Fakultät / Institut für Informatik und Computational Science |
Peer review: | Referiert |
Publishing method: | Open Access |
Institution name at the time of the publication: | Mathematisch-Naturwissenschaftliche Fakultät / Institut für Informatik |