Abstract Machine learning is as growing as fast as concepts such as Big data and the field of
data science in general. The purpose of the systematic review was to analyze scholarly …

Supervised and unsupervised learning algorithms have shown a great potential in
knowledge acquisition from large data sets. Supervised learning reflects the ability of an …

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

Support vector machines with different kernel functions are useful for binary classification. In
this paper, a novel support vector machine model with feature map** is proposed. Feature …

This paper proposes a new approach based on the kernel-free quadratic surface support
vector machine model to handle a binary classification problem with mislabeled information …

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 proposes a novel binary classification approach named Laplacian quadratic
surface optimal margin distribution machine (LapQSODM), for semisupervised learning. This …

Price forecasting in the financial market is one of the most important and challenging tasks in
the field of time series forecasting since it is noisy, non-linear and non-stationary. In this …

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 …

In this paper, a new quadratic kernel-free least square twin support vector machine
(QLSTSVM) is proposed for binary classification problems. The advantage of QLSTSVM is …