When equipped with kernel functions, online learning algorithms are susceptible to the "curse of kernelization" that causes unbounded growth in the model size. To addres...
We propose a highly efficient framework for penalized likelihood kernel methods applied to multiclass models with a large, structured set of classes. As opposed to many previous a...
Recent work in object localization has shown that the use of contextual cues can greatly improve accuracy over models that use appearance features alone. Although many of these mo...
Carolina Galleguillos, Brian McFee, Gert Lanckriet
Previous Multiple Kernel Learning approaches (MKL) employ different kernels by their linear combination. Though some improvements have been achieved over methods using single kerne...
Object/scene detection by discriminative kernel-based classification has gained great interest due to its promising performance and flexibility. In this paper, unlike traditional ...