Unsupervised classification is a highly important task of machine learning methods.
Although achieving great success in supervised classification, support vector machine …

The kernel-free support vector machine (SVM) models are recently developed and studied
to overcome some drawbacks induced by the kernel-based SVM models. To further improve …

In this paper, we propose a new regression method called the∊-kernel-free soft quadratic
surface support vector regression (∊-SQSSVR). After converting the n-dimensional …

This paper presents a novel nonlinear binary classification method, namely the kernel-free
reduced quadratic surface support vector machine with 0-1 loss function and L p-norm …

one-class support vector classification (-OCSVC) has garnered significant attention for its
remarkable performance in handling single-class classification and anomaly detection …

In credit risk assessment, unsupervised classification techniques can be introduced to
reduce human resource expenses and expedite decision-making. Despite the efficacy of …

Multiview learning (MVL) frequently uses support vector machine‐(SVM‐) based models, but
it can be difficult to select appropriate kernel functions and corresponding parameters. Then …

This paper proposes a supervised kernel-free quadratic surface regression method for
feature selection (QSR-FS). The method is to find a quadratic function in each class and …

In this paper, we propose a novel binary classification method called the kernel-free
quadratic surface minimax probability machine (QSMPM), that makes use of the kernel-free …

For multi-class classification problems, a new kernel-free nonlinear classifier is presented,
called the hard quadratic surface least squares regression (HQSLSR). It combines the …