Variational Relevance Vector Machines
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Multiple kernel learning, conic duality, and the SMO algorithm
ICML '04 Proceedings of the twenty-first international conference on Machine learning
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
Principles of multi-kernel data mining
MLDM'05 Proceedings of the 4th international conference on Machine Learning and Data Mining in Pattern Recognition
Active Grading Ensembles for Learning Visual Quality Control from Multiple Humans
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
Selection of Subsets of Ordered Features in Machine Learning
MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
MCS'11 Proceedings of the 10th international conference on Multiple classifier systems
MCS'10 Proceedings of the 9th international conference on Multiple Classifier Systems
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The problem of multi-modal pattern recognition is considered under the assumption that the kernel-based approach is applicable within each particular modality. The Cartesian product of the linear spaces into which the respective kernels embed the output scales of single sensor is employed as an appropriate joint scale corresponding to the idea of combining modalities, actually, at the sensor level. From this point of view, the known kernel fusion techniques, including Relevance and Support Kernel Machines, offer a toolkit of combining pattern recognition modalities. We propose an SVM-based quasi-statistical approach to multi-modal pattern recognition which covers both of these modes of kernel fusion.