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
- V. Franc, S. Sonnenburg.
OCAS optimized cutting plane algorithm for support vector machines.
In Proceedings of the 25nd International Conference on Machine Learning (ICML).
ACM Press, 2008.
[pdf]
- V. Franc, P. Laskov, K.-R.Mueller.
Stopping Conditions for Exact Computation of Leave-One-Out Error in Support Vector Machines.
In Proceedings of the 25nd International Conference on Machine Learning (ICML).
ACM Press, 2008.
[pdf]
- V. Franc, B. Savchynskyy:
Discriminative Learning of Max-Sum Classifiers.
The Journal of Machine Learning Research, vol. 9,
pp. 67--104. January 2008.
[pdf].
- V. Franc, S. Sonnenburg.
Optimized cutting plane algorithm for support vector machines.
Research Report; Electronic Publication 1, Fraunhofer Institute
FIRST, December 2007.
[pdf]
Further results
-
Co-organization of
Pascal Large Scale
Learning Challenge and
ICML'08
Workshop.
The challenge was designed to allow fair and direct
comparison of current large scale classifiers aimed at answering the question
"Which learning method is the most accurate given limited resources?"
-
LIBOCAS
Library implementing OCAS solver for training linear SVM classifiers from
large-scale data.
Last update 02-Dec-2008