Although k-means and its variants are known for their remarkable efficiency, they suffer from
a strong dependence on the prior knowledge of K and the assumption of a circle-like pattern …

With the dramatic improvements in both the capability to collect personal data and the
capability to analyze large amounts of data, increasingly sophisticated and personal insights …

A novel platoon control algorithm for autonomous vehicles is proposed in this paper. There
is an interaction between adjacent vehicles. In order to describe this interaction, a nonlinear …

Object recognition of hyperspectral remote sensing images based on machine learning is
widely applied in many industries. However, the efficiency of the training and recognizing …

Support vector clustering (SVC) is a powerful algorithm for density-based clustering, offering
advantages such as handling arbitrary cluster shapes and determining the number of …

Concerning the good description of arbitrarily shaped clusters, collecting accurate support
vectors (SVs) is critical yet resource-consuming for support vector clustering (SVC). Even …

Support vector data description (SVDD) inspires us in data analysis, adversarial training,
and machine unlearning. However, collecting support vectors requires pricey computation …

With the widespread application of online diagnosis systems, users can upload their
physical characteristics anytime and from anywhere to receive clinical diagnoses. However …

Synthetic data has become increasingly accessible due to remarkable advancements in
machine learning. This data is extremely useful to researchers due to its wide range of …