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Number of items: 7.

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]

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]

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

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

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]

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]

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]

This list was generated on Sat Nov 16 00:22:42 2019 CET.