Twin support vector machineCorrentropy-induced loss functionAlternating iterationHalf-quadratic optimizationAs a variant of the support vector machine (SVM), the twin support vector machine (TSVM) has attracted substantial attention; however, TSVM is sensitive to outliers. To remedy it,this study ...
Twin support vector machine (TSVM), least squares TSVM (LSTSVM) and energy-based LSTSVM (ELS-TSVM) satisfy only empirical risk minimization principle. Moreover, the matrices in their formulations are always positive semi-definite. To overcome these problems, we propose in this paper a robust en...
In this paper, we present a new type of robust capped L1-norm twin support vector machine with privileged information (R-CTSVM+). Based on the TSVM, the capped L1 norm distance is adopted to reduce abnormal points in the same class which are far from the hyperplane, and the bound of ...
"Robust Fuzzy Least Squares Twin Support Vector Machine for Class Imbalance Learning (RFLSTSVM-CIL)"是一种用于处理类别不平衡学习问题的新型机器学习方法。该方法结合了模糊集理论、最小二乘支持向量机和双支持向量机的特点,具有较强的鲁棒性和泛化能力。通过充分利用模糊集的灵活性和最小二乘支持向量机的优势...
The recently proposed twin support vector machine (TWSVM) obtains much faster training speed and comparable performance than classical support vector machine. However, it only considers the empirical risk minimization principle, which leads to poor generalization for real-world applications. In this paper...
Capped Li-norm distance metric-based fast robust twin bounded support vector machine In this paper, to improve the performance of capped L-1-norm twin support vector machine (CTSVM), we first propose a new robust twin bounded support vector... J Ma,L Yang,Q Sun - 《Neurocomputing》 被引...
Recently, there have been several studies utilizing classical machine learning approaches, such as support vector machine (SVM) and RF, and deep learning methods, such as CNN-basic,15,16,38 for outcome prediction or grade classification. To date, several AI methods have been used to assess blas...
Machine learning, trained on quantum mechanics (QM) calculations, is a powerful tool for modeling potential energy surfaces. A critical factor is the quality and diversity of the training dataset. Here we present a highly automated approach to dataset construction and demonstrate the method by buildi...
NPSVM constructs two twin hyperplanes by solving two independent quadratic programming problems and generalizes the well-known twin support vector machine (TWSVM) method. Robustness is conferred on the NPSVM approach by using a probabilistic framework for maximizing model fit, which is cast into two...
One-class support vector machine Let's suppose a training set defines as xi∈Rn,i=1,…,l where xi=(xi1,…,xin) is the normal data in an n-dimensional real space Rn,yi is the corresponding output of xi and l is the number of samples. In the rest, the boldface Greek letters denot...