Twin support vector machine (TWSVM) and twin support vector regression (TSVR) are newly
emerging efficient machine learning techniques which offer promising solutions for …

Due to the advantages of economics, safety, and efficiency, vision-based analysis
techniques have recently gained conspicuous advancements, enabling them to be …

The current mainstream approach of using manual measurements and visual inspections for
crop lodging detection is inefficient, time-consuming, and subjective. An innovative method …

The standard twin support vector machine (TSVM) uses the hinge loss function which leads
to noise sensitivity and instability. In this paper, we propose a novel general twin support …

Multi-label learning paradigm, which aims at dealing with data associated with potential
multiple labels, has attracted a great deal of attention in machine intelligent community. In …

In this paper, we propose an efficient weighted Lagrangian twin support vector machine
(WLTSVM) for the imbalanced data classification based on using different training points for …

Having a multitude of unlabeled data and few labeled ones is a common problem in many
practical applications. A successful methodology to tackle this problem is self-training semi …

In this paper, we construct a least squares version of the recently proposed twin bounded
support vector machine (TBSVM) for binary classification. As a valid classification tool …

Breast cancer is one of the most commonly diagnosed cancer types among women.
Sonography has been regarded as an important imaging modality for diagnosis of breast …

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 …