Multiple kernel learning (MKL) is a principled approach for selecting and combining kernels
for a given recognition task. A number of studies have shown that MKL is a useful tool for …

In recent years, several methods have been proposed to combine multiple kernels instead of
using a single one. These different kernels may correspond to using different notions of …

The cloud radio access network (C-RAN) is a promising paradigm to meet the stringent
requirements of the fifth generation (5G) wireless systems. Meanwhile, the wireless traffic …

Learning linear combinations of multiple kernels is an appealing strategy when the right
choice of features is unknown. Previous approaches to multiple kernel learning (MKL) …

We consider the problem of how to improve the efficiency of Multiple Kernel Learning (MKL).
In literature, MKL is often solved by an alternating approach:(1) the minimization of the …

In many applications involving multi-media data, the definition of similarity between items is
integral to several key tasks, including nearest-neighbor retrieval, classification, and …

Kernel methods have been successfully applied in various applications. To succeed in these
applications, it is crucial to learn a good kernel representation, whose objective is to reveal …

Many state-of-the-art approaches for Multi Kernel Learning (MKL) struggle at finding a
compromise between performance, sparsity of the solution and speed of the optimization …

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 …

In recent years there has been a lot of interest in designing principled classification
algorithms over multiple cues, based on the intuitive notion that using more features should …