Dynamically adapting kernels in support vector machines
Proceedings of the 1998 conference on Advances in neural information processing systems II
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Choosing Multiple Parameters for Support Vector Machines
Machine Learning
An introduction to variable and feature selection
The Journal of Machine Learning Research
Convex Optimization
Learning the Kernel Matrix with Semidefinite Programming
The Journal of Machine Learning Research
Multiple kernel learning, conic duality, and the SMO algorithm
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Large Scale Multiple Kernel Learning
The Journal of Machine Learning Research
More efficiency in multiple kernel learning
Proceedings of the 24th international conference on Machine learning
Robust EEG channel selection across subjects for brain-computer interfaces
EURASIP Journal on Applied Signal Processing
Ensemble of SVMs for improving brain computer interface p300 speller performances
ICANN'05 Proceedings of the 15th international conference on Artificial Neural Networks: biological Inspirations - Volume Part I
Feature shaping for linear SVM classifiers
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
A multiple-kernel support vector regression approach for stock market price forecasting
Expert Systems with Applications: An International Journal
Multiple kernel learning improved by MMD
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications - Volume Part II
Variable Sparsity Kernel Learning
The Journal of Machine Learning Research
Pattern Recognition
lp-Norm Multiple Kernel Learning
The Journal of Machine Learning Research
Multiple Kernel Learning Algorithms
The Journal of Machine Learning Research
Image annotation by composite kernel learning with group structure
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Non-sparse multiple kernel fisher discriminant analysis
The Journal of Machine Learning Research
An efficient multiple-kernel learning for pattern classification
Expert Systems with Applications: An International Journal
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The Support Vector Machine (SVM) is an acknowledged powerful tool for building classifiers, but it lacks flexibility, in the sense that the kernel is chosen prior to learning. Multiple Kernel Learning (MKL) enables to learn the kernel, from an ensemble of basis kernels, whose combination is optimized in the learning process. Here, we propose Composite Kernel Learning to address the situation where distinct components give rise to a group structure among kernels. Our formulation of the learning problem encompasses several setups, putting more or less emphasis on the group structure. We characterize the convexity of the learning problem, and provide a general wrapper algorithm for computing solutions. Finally, we illustrate the behavior of our method on multi-channel data where groups correpond to channels.