Identification of leaf disorder plays an important role in the economic prosperity of any
country. Many parts of a plant can be infected by a virus, fungal, bacteria, and other …

For the regression problem of support vector machine, the solution processes of the most
existing methods use offline datasets, which cannot be realized online. For this problem, this …

Networks both within and outside the world of science are increasingly called upon to
handle very large amounts of data. It is said that we are entering the era of the industrial …

In the face of massive historical data of coal-fired power plants, the method of Deep Sparse
Least Squares Support Vector Regression (DS-LSSVR) is proposed for building the …

In several applications, there are hierarchically-organized time series that can be
aggregated at various levels. In this paper, a novel Support Vector Regression approach is …

We propose a twin support vector quantile regression (TSVQR) to capture the
heterogeneous and asymmetric information in modern data. Using a quantile parameter …

Archives management plays an important role in the current information age. Solving the
problem of identifying and classifying archives is essential for promoting the development of …

Autism spectrum disorder (ASD) is a severe neurodevelopmental disorder that affects an
individual's sensory activity, social interaction, and cognitive abilities. In the mental illnesses …

To improve the generalization performance, we develop a new technique for handling the
impacts of outliers using Lagrangian twin bounded SVM (TBSVM) with kernel fuzzy …

As a variant of Support Vector Machine (SVM), Large Margin Distribution Machine (LDM)
has been validated to outperform SVM both theoretically and experimentally. Due to the …