At present, alloys have broad application prospects in heterogeneous catalysis, due to their
various catalytic active sites produced by their vast element combinations and complex …

Support vector machines (SVMs) are theoretically well-justified machine learning
techniques, which have also been successfully applied to many real-world domains. The …

Generalization remains a central challenge in machine learning. In this work, we propose
Learning from Teaching (LoT), a novel regularization technique for deep neural networks to …

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 …

Online learning from a signal processing perspective There is increased interest in kernel
learning algorithms in neural networks and a growing need for nonlinear adaptive …

Multiple kernel learning (MKL) aims at simultaneously learning a kernel and the associated
predictor in supervised learning settings. For the support vector machine, an efficient and …

In recent years, a large amount of multi-disciplinary research has been conducted on sparse
models and their applications. In statistics and machine learning, the sparsity principle is …

The goal of learning theory is to approximate a function from sample values. To attain this
goal learning theory draws on a variety of diverse subjects, specifically statistics …

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 …