Abstract The Hilbert–Schmidt independence criterion (HSIC) was originally designed to
measure the statistical dependence of the distribution-based Hilbert space embedding in …

Mass spectrometry-based proteomic technique has become indispensable in current
exploration of complex and dynamic biological processes. Instrument development has …

In this paper, a Pearson's correlation coefficient based decision tree (PCC-Tree) is
established and its parallel implementation is developed in the framework of Map-Reduce …

Kernel methods are a cornerstone of classical machine learning. The idea of using quantum
computers to compute kernels has recently attracted attention. Quantum embedding kernels …

Security is a prime challenge in wireless mesh networks. The mesh nodes act as the
backbone of a network when confronting a wide variety of attacks. An intrusion detection …

This paper presents an automatic ECG-based emotion recognition algorithm for human
emotion recognition. First, we adopt a musical induction method to induce participants' real …

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 …

The recent literature indicates that preserving global pairwise sample similarity is of great
importance for feature selection and that many existing selection criteria essentially work in …

Feature selection aims to gain relevant features for improved classification performance and
remove redundant features for reduced computational cost. How to balance these two …

This paper presents a framework of a medical image analysis system for the brain tumor
segmentation and the brain tumor following-up over time using multi-spectral MRI images …