Add to ORKGAbstract We propose a new optimization algorithm for Multiple Kernel Learning (MKL) called SpicyMKL, which is applicable to general convex loss functions and general types of regularization. The proposed SpicyMKL iteratively solves smooth minimization problems. Thus, there is no need of solving SVM, LP, or QP internally. SpicyMKL can be viewed as a proximal minimization method and converges super-linearly. The cost of inner minimiza-tion is roughly proportional to the number of active kernels. Therefore, when we aim for a sparse kernel combination, our algorithm scales well against increasing number of ker-nels. Moreover, we give a general block-norm formulation of MKL that includes non-sparse regularizations, such as elastic-net and p-norm...
We consider the problem of how to improve the efficiency of Multiple Kernel Learning (MKL). In liter...
Multiple kernel learning (MKL) has been proposed for kernel methods by learning the optimal kernel f...
The goal of Multiple Kernel Learning (MKL) is to combine kernels derived from multiple sources in a ...
Many state-of-the-art approaches for Multi Kernel Learning (MKL) struggle at finding a compromise be...
In many applications it is desirable to learn from several kernels. "Multiple kernel learning" (MKL)...
An efficient and general multiple kernel learning (MKL) algorithm has been recently proposed by Sonn...
Multiple Kernel Learning (MKL) can be formulated as a convex-concave minmax optimization problem, wh...
International audienceMultiple kernel learning aims at simultaneously learning a kernel and the asso...
The goal of Multiple Kernel Learning (MKL) is to combine kernels derived from multiple sources in a ...
Many state-of-the-art approaches for Multi Kernel Learning (MKL) struggle at finding a compromise be...
Learning linear combinations of multiple kernels is an appealing strategy when the right choice of f...
We consider the problem of multiple kernel learning (MKL), which can be for-mulated as a convex-conc...
Motivated from real world problems, like object categorization, we study a par-ticular mixed-norm re...
In recent years there has been a lot of interest in designing principled classification algorithms o...
This paper(1) presents novel algorithms and applications for a particular class of mixed-norm regula...
We consider the problem of how to improve the efficiency of Multiple Kernel Learning (MKL). In liter...
Multiple kernel learning (MKL) has been proposed for kernel methods by learning the optimal kernel f...
The goal of Multiple Kernel Learning (MKL) is to combine kernels derived from multiple sources in a ...
Many state-of-the-art approaches for Multi Kernel Learning (MKL) struggle at finding a compromise be...
In many applications it is desirable to learn from several kernels. "Multiple kernel learning" (MKL)...
An efficient and general multiple kernel learning (MKL) algorithm has been recently proposed by Sonn...
Multiple Kernel Learning (MKL) can be formulated as a convex-concave minmax optimization problem, wh...
International audienceMultiple kernel learning aims at simultaneously learning a kernel and the asso...
The goal of Multiple Kernel Learning (MKL) is to combine kernels derived from multiple sources in a ...
Many state-of-the-art approaches for Multi Kernel Learning (MKL) struggle at finding a compromise be...
Learning linear combinations of multiple kernels is an appealing strategy when the right choice of f...
We consider the problem of multiple kernel learning (MKL), which can be for-mulated as a convex-conc...
Motivated from real world problems, like object categorization, we study a par-ticular mixed-norm re...
In recent years there has been a lot of interest in designing principled classification algorithms o...
This paper(1) presents novel algorithms and applications for a particular class of mixed-norm regula...
We consider the problem of how to improve the efficiency of Multiple Kernel Learning (MKL). In liter...
Multiple kernel learning (MKL) has been proposed for kernel methods by learning the optimal kernel f...
The goal of Multiple Kernel Learning (MKL) is to combine kernels derived from multiple sources in a ...