SCOLES

Scalable Online Learning Systems

Marie Curie Intra-European Fellowship grant.
MEIF-CT-2006-042107.


Investigator: Vojtech Franc
Scientist in charge: Klaus-Robert Mueller
Advisor: Pavel Laskov
Host institute: Fraunhofer - FIRST, Intelligent Data Analysis Group,
Project duration: 24 months (from November 1, 2006 to October 31, 2008)

Summary

Classifiers are programs which classify input data to a set of categories. Learning classifiers automatically from examples is subject to the multidisciplinary field of machine learning. The key properties of learning systems are: performance, which depends on how well the used cost function corresponds to the application at hand; scalability, ensuring that memory and time complexity of the learning grows gracefully with data size; and ability to process examples online as they come.

Goals of the project were twofold. First, to develop scalable systems that learn online and use structured (hence more natural) costs. Second, to apply these new learning methods in computer security and bioinformatics.

Publications

Further results


Last update 02-Dec-2008