Invariant kernel functions for pattern analysis and machine learning
Machine Learning
The Effect of Domain Knowledge on Rule Extraction from Support Vector Machines
MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
Invariance in kernel methods by haar-integration kernels
SCIA'05 Proceedings of the 14th Scandinavian conference on Image Analysis
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This paper presents an application of the general sample-to-object approach to the problem of invariant image classification. The approach results in defining new SVM kernels based on tangent vectors that take into account prior information on known invariances. Real data of face images are used for experiments. The presented approach integrates virtual sample and tangent distance methods. We observe a significant increase in performance with respect to standard approaches. The experiments also illustrate (as expected) that prior knowledge becomes more important as the amount of training data decreases.