In recent years, an enormous amount of research has been carried out on support vector
machines (SVMs) and their application in several fields of science. SVMs are one of the …

Support vector machines (SVMs) are a supervised classifier successfully applied in a
plethora of real-life applications. However, they suffer from the important shortcomings of …

Despite various approaches proposed to smooth the hyperspectral images (HSIs) before
feature extraction, the efficacy is still affected by the noise, even using the corrected dataset …

Accurate solar photovoltaic (PV) power forecasting is an essential tool for mitigating the
negative effects caused by the uncertainty of PV output power in systems with high …

In the new generation of aircraft, electro-mechanical actuators (EMA) have been replacing
the conventional hydraulic versions. Despite the fact that a failure of this system can …

As a powerful predictive technique based on machine learning, the maximum entropy
(MaxEnt) model has been widely used in geographic modeling. However, its performance in …

Support vector machines (SVMs) are powerful classifiers that have high computational
complexity in the training phase, which can limit their applicability to large datasets. An …

Support vector machines (SVMs) have been exploited in a plethora of real-life classification
and regression tasks, and are one of the most researched supervised learners. However …

Training support vector machines (SVMs) for pixel-based feature extraction purposes from
aerial images requires selecting representative pixels (instances) as a training dataset. In …

Support vector machine (SVM) is a popular supervised machine learning technique
extensively applied to various real-life applications. A weakness of SVM, though, is that its …