Distance metric learning is a branch of machine learning that aims to learn distances from
the data, which enhances the performance of similarity-based algorithms. This tutorial …

The Kullback–Leibler divergence (KLD), which is widely used to measure the similarity
between two distributions, plays an important role in many applications. In this article, we …

Background Frailty is a dynamic and complex geriatric condition characterized by multi-
domain declines in physiological, gait and cognitive function. This study examined whether …

It aims to apply the neural network algorithm to the mining of educational resource data and
provide new ideas for the intelligent development of teaching evaluation. The Apriori …

Distance metric learning is a branch of machine learning that aims to learn distances from
the data, which enhances the performance of similarity-based algorithms. This tutorial …

Internet of Things (IoT) radio networks are becoming popular in several scenarios for short-
range applications (eg, wearables and home security) and medium-range applications (eg …

Distance metric learning has been a promising technology to improve the performance of
algorithms related to distance metrics. The existing distance metric learning methods are …

Accurately classifying sceneries with different spatial configurations is an indispensable
technique in computer vision and intelligent systems, for example, scene parsing, robot …

Polycystic ovary syndrome (PCOS) and endometrial carcinoma (EC) are gynecological
conditions that have attracted significant attention due to the higher prevalence of EC in …

Learning a proper distance for clustering from prior knowledge falls into the realm of
semisupervised fuzzy clustering. Although most existing learning methods take prior …