Kernel function implicitly maps data from its original space to a higher dimensional feature
space. Kernel based machine learning algorithms are typically applied to data that is not …

Kernel methods are known to be a state-of-the-art classification technique. Nevertheless, the
training and prediction cost is expensive for large data. On the other hand, linear classifiers …

Machine learning, especially neural network algorithm, gets more attentions in the last
decades. With the in-depth study of intelligence algorithm and network structure, machine …

In this paper, we propose a data-adaptive non-parametric kernel learning framework in
margin based kernel methods. In model formulation, given an initial kernel matrix, a data …

Kernel learning methods are among the most effective learning methods and have been
vigorously studied in the past decades. However, when tackling with complicated tasks …

The performance of a kernel method often depends mainly on the appropriate choice of a
kernel function. In this study, we present a data-dependent method for scaling the kernel …

In recent years, the machine learning community has witnessed a tremendous growth in the
development of kernel-based learning algorithms. However, the performance of this class of …

Previous studies of Non-Parametric Kernel (NPK) learning usually reduce to solving some
Semi-Definite Programming (SDP) problem by a standard SDP solver. However, time …

The success of kernel-based learning methods depends on the choice of kernel. Recently,
kernel learning methods have been proposed that use data to select the most appropriate …

The data from real world usually have nonlinear geometric structure, which are often
assumed to lie on or close to a low-dimensional manifold in a high-dimensional space. How …