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Multiple kernel learning (MKL) is a widely used technique for kernel design. Its principle consists in learning, for a given support vector classifier, the most suitable convex (or sparse) linear combination of standard elementary kernels. However, these combinations are shallow and often powerless to capture the actual similarity between highly semantic data, especially for challenging classification...
Satellite image change detection consists in defining criteria that localize relevant changes while discard irrelevant ones. This problem is known to be challenging due to several factors including illumination changes, occlusion and also subjectivity of users. In this paper, we introduce a novel change detection algorithm based on relevance feedback and a new variant of canonical correlation analysis...
It is commonly agreed that the success of support vector machines (SVMs), is highly dependent on the choice of particular similarity functions referred to as kernels. The latter are usually handcrafted or designed using appropriate optimization schemes. Multiple kernel learning (MKL) is one possible scheme that designs kernels as sparse or convex linear combinations of existing elementary functions...
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