Over the past two decades, one-class classification (OCC) becomes very popular due to its
diversified applicability in data mining and pattern recognition problems. Concerning to …

At present, the excellent performance of support vector machine (SVM) has made it
successfully applied in many fields. However, when SVM is used for two-dimensional matrix …

Twin support vector machines (TWSVM), a novel machine learning algorithm developing
from traditional support vector machines (SVM), is one of the typical nonparallel support …

Fuzzy twin support vector machine (FTSVM) is an effective machine learning technique that
is able to overcome the negative impact of noise and outliers in tackling data classification …

Accurate annual runoff forecasting is of great significance for water resources management
and timely flood control. However, nonlinear and non-stationary runoff series and the …

Support vector machines are arguably one of the most successful methods for data
classification, but when using them in regression problems, literature suggests that their …

Abstract Least Squares Twin Support Vector Machine (LSTSVM) is a binary classifier and
the extension of it to multiclass is still an ongoing research issue. In this paper, we extended …

Usually the real-world (RW) datasets are imbalanced in nature, ie, there is a significant
difference between the number of negative and positive class samples in the datasets …

Considering that the displacement of super high arch dams is sensitive to temperature
variations and dam temperature field is essentially modulated by the ambient temperature, a …

In this paper, we propose a robust smooth pinball loss nonparallel support vector machine
(SpinNSVM) for binary classification. We first define a smooth pinball loss function, which is …