  In recent years, supervised and semi-supervised machine learning methods such
as neural networks, support vector machines (SVM), and semi-supervised support
vector machines (S4VM) have been widely used in quantum entanglement and
quantum steering verification problems. However, few studies have focused on
detecting genuine multipartite entanglement based on machine learning. Here, we
investigate supervised and semi-supervised machine learning for detecting
genuine multipartite entanglement of three-qubit states. We randomly generate
three-qubit density matrices, and train an SVM for the detection of genuine
multipartite entangled states. Moreover, we improve the training method of
S4VM, which optimizes the grouping of prediction samples and then performs
iterative predictions. Through numerical simulation, it is confirmed that this
method can significantly improve the prediction accuracy.
