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 …

This paper presents new and effective algorithms for learning kernels. In particular, as
shown by our empirical results, these algorithms consistently outperform the so-called …

The success of kernel methods is very much dependent on the choice of kernel. Kernel
design and learning a kernel from the data require evaluation measures to assess the …

Since the introduction of support vector machines, we have witnessed the huge
development in theory, models, and applications of what is so-called kernel-based methods …

For improving forecasting accuracy and trading performance, this paper proposes a new
multi‐objective least squares support vector machine with mixture kernels to forecast asset …

The choice of the kernel is critical to the success of many learning algorithms but it is
typically left to the user. Instead, the training data can be used to learn the kernel by …

In this paper we investigate conditions on the features of a continuous kernel so that it may
approximate an arbitrary continuous target function uniformly on any compact subset of the …

Meta-learning can successfully acquire useful inductive biases from data. Yet, its
generalization properties to unseen learning tasks are poorly understood. Particularly if the …

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) …

Kernel association (KA) in statistical pattern recognition used for classification and prediction
have recently emerged in a machine learning and signal processing context. This survey …