Abstract The Support Vector Machine (SVM) method is a method that is widely used in the
classification process. The success of the classification of the SVM method depends on the …

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 …

The absolute positioning accuracy of an industrial robot arm is vital for advancing
manufacturing-related applications like automatic assembly, which can be improved via the …

Coastal wetland soil organic carbon (CW-SOC) is crucial for both “blue carbon” and carbon
sequestration. It is of great significance to understand the content of soil organic carbon …

Obstacles in front of a train pose a significant threat to traffic safety, and many accidents
happen under shunting mode when the speed of a train is below 45 km/h. The existing track …

The AdaBoost algorithm has the superiority of resisting overfitting. Understanding the
mysteries of this phenomenon is a very fascinating fundamental theoretical problem. Many …

Due to the lack of data available for training, deep learning hardly performed well in the field
of garbage image classification. We choose the TrashNet data set which is widely used in …

The efficient classification of remote sensing images (RSIs) has become the key of remote
sensing application. To tackle the high computational cost in the traditional classification …

The generalization error of conventional support vector machine (SVM) depends on the ratio
of two factors; radius and margin. The traditional SVM aims to maximize margin but ignore …

Aiming to address the issue of deception jamming generated by digital radio frequency
memory (DRFM), this study proposes a feature extraction algorithm based on variational …