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Articles by Guolin Yu
Robust Twin Extreme Learning Machine Based on Soft Truncated Capped L1-Norm Loss Function
1. Introduction Extreme learning machine (ELM) [1,2] was proposed by Huang et al. on a single hidden layer feedforward network. Compared with traditional training methods, ELM has advantages in learning speed and generalization performance. This is because ELM differs from traditional methods, that is, the input consists of randomly generated weights and bias values of the hidden layer nodes, allowing for the quick and effective determination of output weights.
Strict Vector Equilibrium Problems of Multi-Product Supply-Demand Networks with Capacity Constraints and Uncertain Demands
1. Introduction The study of the supply–demand network equilibrium models has been the subject of great interest due to their theoretical challenges and practical application. The fundamental principle is Wardrop’s equilibrium principle [1], which states that users in transport networks choose one of the paths among all the paths joining the same origin–destination (OD) pair at minimum cost. After Wardrop, many scholars have proposed various network equilibrium models based on a single criterion.
Distance Metric Optimization-Driven Neural Network Learning Framework for Pattern Classification
This is an early access version, the complete PDF, HTML, and XML versions will be available soon. Open AccessArticle by Yimeng Jiang , Guolin Yu * and Jun Ma School of Mathematics and Information Sciences, North Minzu University, Yinchuan 750021, China * Author to whom correspondence should be addressed.
A Novel Robust Metric Distance Optimization-Driven Manifold Learning Framework for Semi-Supervised Pattern Classification
This is an early access version, the complete PDF, HTML, and XML versions will be available soon. Open AccessArticle by Bao Ma , Jun Ma * and Guolin Yu School of Mathematics and Information Sciences, North Minzu University, Yinchuan 750021, China * Author to whom correspondence should be addressed.
Optimality Conditions of the Approximate Efficiency for Nonsmooth Robust Multiobjective Fractional Semi-Infinite Optimization Problems
This is an early access version, the complete PDF, HTML, and XML versions will be available soon. Open AccessArticle by Liu Gao 1, Guolin Yu 1,* and Wenyan Han 2 1 School of Mathematics and Information Science, North Minzu University Yinchuan 750021, China 2 School of Mathematics and Statistics, Ningxia University Yinchuan 750021, China * Author to whom correspondence should be addressed.
Capped L2,p-Norm Metric Based on Robust Twin Support Vector Machine with Welsch Loss
1. Introduction The support vector machine (SVM) [1,2,3,4,5] is designed for the binary classification problem, which is the development basis of other classifiers in machine learning due to its superior performance in text classification tasks. The SVM is widely used in data classification and regression analysis problems, such as artificial intelligence, speech recognition, remote image analysis, financial management, etc.
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