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 notion of kernels, recently introduced, has drawn much interest as it allows one to obtain
nonlinear algorithms from linear ones in a simple and elegant manner. This, in conjunction …

Pattern Analysis is the process of finding general relations in a set of data, and forms the
core of many disciplines, from neural networks, to so-called syntactical pattern recognition …

Motivation: Computational approaches to protein function prediction infer protein function by
finding proteins with similar sequence, structure, surface clefts, chemical properties, amino …

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 …

While classical kernel-based classifiers are based on a single kernel, in practice it is often
desirable to base classifiers on combinations of multiple kernels. Lanckriet et al.(2004) …

While classical kernel-based learning algorithms are based on a single kernel, in practice it
is often desirable to use multiple kernels. Lanckriet et al.(2004) considered conic …

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 …

The advantages of discriminative learning algorithms and kernel machines are combined
with generative modeling using a novel kernel between distributions. In the probability …

In this paper, we propose the class-boundaryalignment algorithm to augment SVMs to deal
with imbalanced training-data problems posed by many emerging applications (eg, image …