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Michael Brückner
Max-Planck-Institut für Informatik
Department RG2: Machine Learning
Building 46.1, Room 428
Stuhlsatzenhausweg 85
66123 Saarbrücken
Germany
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Phone: +49 681 9325 528
Fax: +49 681 9325 599
PGP ID: 0x09EC932D
- Adversarial Classification and Email Filtering
- Kernel Machines and Statistical Learning Algorithms
- Correcting Sample Selection Bias using unlabeled Data
- Brückner M., Haider P., and Scheffer T.: Highly Scalable Discriminative Spam Filtering.
In: Proceedings of the 15th Text REtrieval Conference (TREC 2006), Gaithersburg, USA, 2006.
Abstract - This paper discusses several lessons learned from the SpamTREC 2006 challenge. We
discuss issues related to decoding, preprocessing, and tokenization of email messages. Using
the Winnow algorithm with orthogonal sparse bigram features, we construct an efficient,
highly scalable incremental classifier, trained to maximize a discriminative optimization
criterion. The algorithm easily scales to millions of training messages and millions of
features. We address the composition of training corpora and discuss experiments that
guide the construction of our SpamTREC entry. We describe our submission for the filtering
tasks with periodical re-training and active learning strategies, and report on the evaluation
on the publicly available corpora.
- Brückner M.: The p-Center Machine.
In: Proceedings of the International Joint Conference on Neural Networks, Montreal, Canada, 2005, ISBN: 0-7803-9049-0, pp. 1000-1005.
Abstract - We present a new approach to find an optimal large margin classifier based on the p-center which was proposed by
Moretti in 2003. Starting with the p-Center of a general polytope, we extend this definition to a polyhedral cone, and introduce an
algorithm approximating the p-Center of the version space, which we call p-Center machine (PCM). In addition, we present a
large-scale and a soft boundary version of the PCM, and compare their performance to the support vector machine and the Bayes
point machine. It turns out that the p-Center is close to the Bayes point and is similar in performance to the support vector
machine as well as the Bayes point machine. Additionally, the proposed algorithm is highly parallelizable and thus very efficient
in terms of computational effort.
- Brückner M. and Dilger W.: A soft Bayes perceptron.
In: Proceedings of the International Joint Conference on Neural Networks, Montreal, Canada, 2005, ISBN: 0-7803-9049-0, pp. 2064-2069.
Abstract - The kernel perceptron is one of the simplest and fastest kernel machines, its performance, however, is inferior to
other well known kernel machines. We introduce an algorithm that combines several approaches, mainly Herbrich's large-scale Bayes point
machine and the soft perceptron in order to improve the kernel perceptron. Our experiments, which were based on standard benchmark
datasets, show that the performance of the perceptron can be improved significantly with similar computational effort.
Seminar Network Mining (WS 2006/07) at Humboldt Universität zu Berlin
Seminar Kernel Machines (SS 2006) at Humboldt Universität zu Berlin
January 2007 - present:
Researcher at Max Planck Institute for Computer Science
July 2005 - December 2006:
Researcher at
Knowledge Management Group, Humboldt Universität zu Berlin
- Kernel Machines - A central source of information on kernel based methods,
including support vector machines, Gaussian processes.
- StopSpamAlliance.org - The StopSpamAlliance is a joint initiative to gather
information and resources on combating spam. This initiative was undertaken by APEC, the EU’s CNSA, ITU, the London Action
Plan, OECD and the Seoul-Melbourne Anti-Spam group.
- Anti-Spam Research Group - The Anti-Spam Research Group (ASRG)
investigates tools and techniques to mitigate the effects of spam. ASRG is an Internet Research Task Force (IRTF) Research Group.
- Sports such as scuba diving, skiing, squash etc.
- Travelling around the World
