Machine learning and kernel methods for computer vision

author: Francis R. Bach, INRIA - SIERRA project-team
published: Dec. 5, 2008,   recorded: November 2008,   views: 17448


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Kernel methods are a new theoretical and algorithmic framework for machine learning. By representing data through well defined dot-products, referred to as kernels, they allow to use classical linear supervised machine learning algorithms to non linear settings and to non vectorial data. A major issue when applying these methods to image processing or computer vision is the choice of the kernel. I will present recent advances in the design of kernels for images that take into account the natural structure of images.

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