الفهرس 
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Abstract
Ranking is the central problem for many applications such as web search, recommendation
systems, and visual comparison of images. In this work, the multiple kernel learning
framework is generalized for the learning to rank problem. This approach extends the
existing learning to rank algorithms by considering multiple kernel learning and consequently
improves their e↵ectiveness. The proposed approach provides the convenience
of fusing di↵erent features for describing the underlying data. As an application to our
approach, the problem of visual image comparison is studied. Several visual features
are used for describing the images and multiple kernel learning is adopted to find an
optimal feature fusion. Experimental results on three challenging datasets show that our
approach outperforms the state-of-the art and is significantly more efficient in runtime.