Structured large margin learning
Defeng Wang, Yeung, Ng, Tsang, Xizhao Wang · 2005
This paper presents a new large margin learning approach, namely structured large margin machine (SLMM), which incorporates both merits of "structured" learning models and advantages of large margin learning schemes. The promising features of this model, such as enhanced generalization ability, scalability, extensibility, and noise tolerance, are demonstrated theoretically and empirically. SLMM is of theoretical importance because it is a generalization of learning models like SVM, MPM, LDA, and M4 etc. Moreover, it provides a novel insight into the study of learning methods and forms a foundation for conceiving other "structured" classifiers.