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Number of items at this level: 49.

Tavakol, Maryam (2019):
Contextual Models for Sequential Recommendation.
Darmstadt, Technische Universität, [Online-Edition: https://tuprints.ulb.tu-darmstadt.de/8667],
[Ph.D. Thesis]

Binder, A. and Nakajima, S. and Kloft, M. and Müller, C. and Samek, W. and Brefeld, U. and Müller, K.-R. and Kawanabe, M. (2012):
Insights from Classifying Visual Concepts with Multiple Kernel Learning.
In: PLoS ONE, 7(8):e38897, [Article]

Görnitz, N. and Kloft, M. and Rieck, K. and Brefeld, U. (2012):
Toward Supervised Anomaly Detection.
In: Journal of Artificial Intelligence Research, accepted, [Article]

Haider, P. and Chiarandini, L. and Brefeld, U. and Jaimes, A. (2012):
Dynamic Contextual Models for User Interaction on the Web.
In: ECML/PKDD Workshop on Mining and Exploiting Interpretable Local Patterns (I-PAT), [Conference or Workshop Item]

Haider, P. and Chiarandini, L. and Brefeld, U. (2012):
Discriminative Clustering for Market Segmentation.
In: Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining, [Conference or Workshop Item]

Kloft, M. and Brefeld, U. and Sonnenburg, S. and Zien, A. (2011):
lp-Norm Multiple Kernel Learning.
12(Mar), In: Journal of Machine Learning Research, pp. 953-997, [Article]

Rathke, F. and Hansen, K. and Brefeld, U. and Müller, K.-R. (2011):
StructRank: A New Approach for Ligand-Based Virtual Screening.
51, In: Journal of Chemical Information Modeling, pp. 83-92, [Article]

Binder, A. and Nakajima, S. and Kloft, M. and Müller, C. and Samek, W. and Brefeld, U. and Müller, K.-R. and Kawanabe, M. (2011):
Insights from Classifying Visual Concepts with Multiple Kernel Learning.
[Report]

Haider, P. and Chiarandini, L. and Brefeld, U. (2011):
Behavioral User Models for Yahoo! News.
In: Proceedings of TechPulse, [Conference or Workshop Item]

Brefeld, U. and Cambazoglu, B. B. and Junqueira, F. P. (2011):
Document Assignment in Multi-Site Search Engines.
In: Proceedings of the International Conference on Web Search and Data Mining, [Conference or Workshop Item]

Fernandes, E. R. and Brefeld, U. (2011):
Learning from Partially Annotated Sequences.
In: Proceedings of the European Conference on Machine Learning, [Conference or Workshop Item]

Amodeo, G. and Blanco, R. and Brefeld, U. (2011):
Hybrid Models for Future Event Prediction.
In: Proceedings of the International ACM Conference on Information and Knowledge Management, [Conference or Workshop Item]

Giannopoulos, G. and Brefeld, U. and Dalamagas, T. and Sellis, T. (2011):
Learning to Rank User Intent.
In: Proceedings of the International ACM Conference on Information and Knowledge Management, [Conference or Workshop Item]

Brefeld, U. and Getoor, L. and Macskassy, S. A. (2010):
Eighth workshop on mining and learning with graphs.
12(2), In: SIGKDD Explorations, pp. 63-65, [Article]

Brefeld, U. and Getoor, L. and Macskassy, S. A. (eds.) (2010):
MLG 2010: Proceedings of the Eighth Workshop on Mining and Learning with Graphs.
New York, NY, USA, ACM, [Book]

Kloft, M. and Brefeld, U. and Sonnenburg, S. and Zien, A. (2010):
Non-Sparse Regularization and Efficient Training with Multiple Kernels.
(UCB/EECS-2010-21), [Report]

Kloft, M. and Brefeld, U. and Sonnenburg, S. and Laskov, P. and Müller, K.-R. and Zien, A. (2010):
Efficient and Accurate $ell_p$-norm Multiple Kernel Learning.
In: Advances in Neural Information Processing Systems, [Conference or Workshop Item]

Rieck, K. and Krüger, T. and Brefeld, U. and Müller, K.-R. (2010):
Approximate Tree Kernels.
11, In: Journal of Machine Learning Research, pp. 555-580, [Article]

Brefeld, U. and Piskorski, J. and Yangarber, R. (eds.) (2009):
Proceedings of the UCMedia Workshop on Mining User Generated Content for Security.
[Book]

Kloft, M. and Brefeld, U. and Sonnenburg, S. and Zien, A. (2009):
Comparing Sparse and Non-sparse Multiple Kernel Learning.
In: Proceedings of the NIPS Workshop on Understanding Multiple Kernel Learning Methods, [Conference or Workshop Item]

Kloft, M. and Brefeld, U. and Sonnenburg, S. and Zien, A. and Laskov, P. and Müller, K.-R. (2009):
Learning Non-Sparse Kernel Mixtures.
In: Proceedings of the PASCAL2 Workshop on Sparsity in Machine Learning and Statistics, [Conference or Workshop Item]

Görnitz, N. and Kloft, M. and Rieck, K. and Brefeld, U. (2009):
Active Learning for Network Intrusion Detection.
In: Proceedings of the CCS Workshop on Security and Artificial Intelligence, [Conference or Workshop Item]

Nakajima, S. and Binder, A. and Müller, C. and Wojcikiewicz, W. and Kloft, M. and Brefeld, U. and Müller, K.-R. and Kawanabe, M. (2009):
Multiple Kernel Learning for Object Classification.
In: Proceedings of the 12th Workshop on Information-based Induction Sciences, [Conference or Workshop Item]

Kloft, M. and Nakajima, S. and Brefeld, U. (2009):
Feature Selection for Density Level-Sets.
In: Proceedings of the European Conference on Machine Learning, [Conference or Workshop Item]

Görnitz, N. and Kloft, M. and Brefeld, U. (2009):
Active and Semi-supervised Data Domain Description.
In: Proceedings of the European Conference on Machine Learning, [Conference or Workshop Item]

Binder, A. and Kawanabe, M. and Brefeld, U. (2009):
Efficient Classification of Images with Taxonomies.
In: Proceedings of the Asian Conference on Computer Vision, [Conference or Workshop Item]

Blohm, S. and Brefeld, U. and Jungermann, F. and Yangarber, R. (eds.) (2008):
Proceedings of the {ECML} Workshop on High-level Information Extraction.
[Book]

Kloft, M. and Brefeld, U. and Düssel, P. and Gehl, C. and Laskov, P. (2008):
Automatic feature selection for anomaly detection.
In: Proceedings of the First ACM Workshop on AISec, [Conference or Workshop Item]

Kloft, M. and Brefeld, U. and Laskov, P. and Sonnenburg, S. (2008):
Non-sparse multiple kernel learning.
In: Proceedings of the NIPS Workshop on Kernel Learning: Automatic Selection of Optimal Kernels, [Conference or Workshop Item]

Klein, T. and Brefeld, U. and Scheffer, T. (2008):
Exact and approximate inference for annotating graphs with structural {SVMs}.
In: Proceedings of the European Conference on Machine Learning, [Conference or Workshop Item]

Rieck, K. and Brefeld, U. and Krüger, T. (2008):
Approximate Kernels for Trees.
(5/2008), [Report]

Brefeld, U. and Haider, P. and Scheffer, T. (2007):
Supervised clustering for spam detection in data streams.
In: Proceedings of the NIPS Workshop on Machine Learning in Adversarial Environments for Computer Security, [Conference or Workshop Item]

Brefeld, U. and Klein, T. and Scheffer, T. (2007):
Support vector machines for collective inference.
In: Proceedings of the Workshop on Mining and Learning with Graphs, [Conference or Workshop Item]

Haider, P. and Brefeld, U. and Scheffer, T. (2007):
Supervised clustering of streaming data for email batch detection.
In: Proceedings of the International Conference on Machine Learning, [Conference or Workshop Item]

Zien, A. and Brefeld, U. and Scheffer, T. (2007):
Transductive support vector machines for structured variables.
In: Proceedings of the International Conference on Machine Learning, [Conference or Workshop Item]

Brefeld, U. and Joachims, T. and Taskar, B. and Xing, E. P. (eds.) (2006):
Proceedings of the {ICML} Workshop on Learning in Structured Output Spaces.
[Book]

Brefeld, U. and Gärtner, T. and Scheffer, T. and Wrobel, S. (2006):
Efficient co-regularised least squares regression.
In: Proceedings of the International Conference on Machine Learning, [Conference or Workshop Item]

Brefeld, U. and Scheffer, T. (2006):
Semi-supervised learning for structured output variables.
In: Proceedings of the International Conference on Machine Learning, [Conference or Workshop Item]

Hakenberg, J. and Bickel, S. and Plake, C. and Brefeld, U. and Zahn, H. and Faulstich, L. and Leser, U. and Scheffer, T. (2005):
Systematic feature evaluation for gene name recognition.
6(1), In: BMC Bioinformatics, ISSN 1471-2105, DOI: 10.1186/1471-2105-6-S1-S9,
[Article]

Brefeld, U. and Scheffer, T. (2005):
{AUC} maximizing support vector learning.
In: Proceedings of the ICML Workshop on ROC Analysis in Machine Learning, [Conference or Workshop Item]

Brefeld, U. and Büscher, C. and Scheffer, T. (2005):
Multi-view discriminative sequential learning.
In: Proceedings of the European Conference on Machine Learning, [Conference or Workshop Item]

Brefeld, U. and Büscher, C. and Scheffer, T. (2005):
Multi-view Hidden Markov Perceptrons.
In: Proceedings of the German Workshop on Machine Learning (FGML), [Conference or Workshop Item]

Abecker, A. and Bickel, S. and Brefeld, U. and Drost, I. and Henze, N. and Herden, O. and Minor, M. and Scheffer, T. and Stojanovic, L. and Weibelzahl, S. (eds.) (2004):
Proceedings of the Workshops on Learning, Knowledge Discovery, and Adaptivity (LWA).
[Book]

Brefeld, U. and Bickel, S. and Scheffer, T. (2004):
Multi-View Lernen.
In: Proceedings of the German Workshop on Machine Learning (FGML), [Conference or Workshop Item]

Bickel, S. and Brefeld, U. and Faulstich, L. and Hakenberg, J. and Leser, U. and Plake, C. and Scheffer, T. (2004):
A Support Vector Machine Classifier for Gene Name Recognition.
In: Proceedings of BioCreative / EMBO Workshop: A Critical Assessment of Text Mining Methods in Molecular Biology, [Conference or Workshop Item]

Brefeld, U. and Scheffer, T. (2004):
Co-{EM} support vector learning.
In: Proceedings of the International Conference on Machine Learning, [Conference or Workshop Item]

Geibel, P. and Brefeld, U. and Wysotzki, F. (2004):
Perceptron and {SVM} Learning with Generalized Cost Models.
8(5), In: Intelligent Data Analysis, pp. 439-455, [Article]

Geibel, P. and Brefeld, U. and Wysotzki, F. (2003):
Learning linear classifiers sensitive to example dependent and noisy costs.
In: Proceedings of the International Symposium on Intelligent Data Analysis, [Conference or Workshop Item]

Brefeld, U. and Geibel, P. and Wysotzki, F. (2003):
Support vector machines with example dependent costs.
In: Proceedings of the European Conference on Machine Learning, [Conference or Workshop Item]

This list was generated on Sun Feb 23 01:56:20 2020 CET.