Multi-class classification is an important and challenging research topic with many real-life
applications. The problem is much harder than the classical binary classification, especially …

As a well-known machine learning technique, support vector machine (SVM) with a kernel
function achieves much success in nonlinear binary classification tasks. Recently, some …

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 binary classification, kernel-free quadratic support vector machines are proposed to avoid
difficulties such as finding appropriate kernel functions or tuning their hyper-parameters …

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 …

Support vector machine (SVM) is a popular and important machine learning technique
based on the central idea of maximizing the minimummargin, ie, the smallest distance from …

Infrared images often carry obvious streak noises due to the non-uniformity of the infrared
detector and the readout circuit. These streak noises greatly affect the image quality, adding …

The weighted complementarity problem (denoted by WCP) significantly extends the general
complementarity problem and can be used for modeling a larger class of problems from …

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 …

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 …