The Paper
- The paper can be downloaded here: [paper+pdf] [bibtex]
- The original publication is available from http://www.plosone.org.
Source Code
- The employed MKL implementation can be found in the Shogun Toolbox.
The toolbox comes with examples that explain the usage of the MKL implementations:
- Additional matlab wrapper scripts for Shogun-based MKL training/testing (including validation) are available from http://doc.ml.tu-berlin.de/nonsparse_mkl/
Datasets
References
[1] A. Binder, S. Nakajima, M. Kloft, C. Mller, W. Samek, U. Brefeld, K.-R. Mller,
and M. Kawanabe. Insights from classifying visual concepts with multiple kernel
learning. PLoS ONE, 7(8):e38897, 08 2012. doi: 10.1371/journal.pone.0038897.
Page style borrowed from Afshin Rostamizadeh
Supplementary material to
A. Binder, S. Nakajima, M. Kloft, C. Müller, W. Samek, U. Brefeld, K.-R. Müller, and M. Kawanabe. Insights from Classifying Visual Concepts with Multiple Kernel Learning.. PLoS ONE, 2012.Authors
- Alexander Binder
- Fraunhofer FIRST / TU Berlin
- Shinichi Nakajima
- Nikon Corporation
- Marius Kloft
- TU Berlin
- Christina Müller
- TU Berlin
- Ulf Brefeld
- Universität Bonn / Zalando
- Wojciech Samek
- TU Berlin
- Klaus-Robert Müller
- TU Berlin
- Motoaki Kawanabe
- ATR Brain Information Communication Research Laboratory
Links:
- Shogun Toolbox
- (Toolbox with Lp-norm MKL implementations)
- PLoS ONE