As having the closed form solution, the least squares support vector machine (LSSVM) has
been widely used for classification and regression problems owing to its competitive …

For support vector machine (SVM) learning, least squares SVM (LSSVM), derived by duality
LSSVM (D-LSSVM), is a widely used model, because it has an explicit solution. One obvious …

The loss of lake area significantly influences the climate change in a region, and this loss
represents a serious and unavoidable challenge to maintaining ecological sustainability …

Support vector machine (SVM) has always been one of the most successful learning
methods, with the idea of structural risk minimization which minimizes the upper bound of …

In this article, an improved nonlinear smooth twin support vector regression (NSTSVR)
model is proposed for the modeling and compensating of the transmitter nonlinearities …

Over the past two decades, support vector machines (SVMs) have become a popular
supervised machine learning model, and plenty of distinct algorithms are designed …

Least squares projection twin support vector machine (LSPTSVM) has faster computing
speed than classical least squares support vector machine (LSSVM). However, LSPTSVM is …

Based on Hoeffding's inequality, many popular regression and classification models in
supervised learning relax the expected risk minimization problem to the empirical risk …

The efficient representation of the accurate corporate value on the stock price is vital to
investors and fund managers that desire to optimize the net worth of the overall stock …

The efficient representation of the accurate corporate value on the stock price is vital to
investors and fund managers that desire to optimise the net worth of the overall stock …