The nature of statistical learning theory
The nature of statistical learning theory
Kernel principal component analysis
Advances in kernel methods
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
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
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Generalized Discriminant Analysis Using a Kernel Approach
Neural Computation
Optimal kernel selection in Kernel Fisher discriminant analysis
ICML '06 Proceedings of the 23rd international conference on Machine learning
The challenge problem for automated detection of 101 semantic concepts in multimedia
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Large Scale Multiple Kernel Learning
The Journal of Machine Learning Research
Multiclass multiple kernel learning
Proceedings of the 24th international conference on Machine learning
Multi-class Discriminant Kernel Learning via Convex Programming
The Journal of Machine Learning Research
Efficient Kernel Discriminant Analysis via Spectral Regression
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Kernel Codebooks for Scene Categorization
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
Non-sparse Multiple Kernel Learning for Fisher Discriminant Analysis
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
L2 regularization for learning kernels
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Input space versus feature space in kernel-based methods
IEEE Transactions on Neural Networks
Generalized augmentation of multiple kernels
MCS'11 Proceedings of the 10th international conference on Multiple classifier systems
Two-stage augmented kernel matrix for object recognition
MCS'11 Proceedings of the 10th international conference on Multiple classifier systems
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In this paper we present a novel approach to combining multiple kernels where the kernels are computed from different information channels. In contrast to traditional methods that learn a linear combination of n kernels of size m ×m, resulting in m coefficients in the trained classifier, we propose a method that can learn n ×m coefficients. This allows to assign different importance to the information channel per example rather than per kernel. We analyse the proposed kernel combination in empirical feature space and provide its geometrical interpretation. We validate the approach on both UCI datasets and an object recognition dataset, and demonstrate that it leads to classification improvements.