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不变性常识(Invariance)与支持向量机(Support Vector Machine,SVM)的融合技术是近年来支持向量机研究的重点之一,将不变性常识融合于学习模型,有助于提高模型的泛化能力。探讨不变性支持向量机(InvarianceSVM)的形式化描述、目前发展状况及不变性常识与SVM融合的主要技术,并分析各方法的优缺点。
The fusion technology of Invariance and Support Vector Machine (SVM) is one of the focuses of support vector machine (SVM) in recent years. Integrating the invariance common knowledge into the learning model helps to improve the generalization ability of the model . This paper discusses the formal description of invariance support vector machines (Invariance SVM), the current development and the main techniques of invariance common knowledge and SVM, and analyzes the advantages and disadvantages of each method.