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统计学习理论(Statistical Learning Theory,SLT)是一种专门研究小样本情况下机器学习规律的理论,作为统计学习理论的VC维理论和结构风险最小化(Structure Risk Minimization,SRM)原则的具体实现算法支持向量机(support vector machinse,SVM),集优化、核(Kernel)、最佳推广能力等特点于一身,其出色的学习能力被广泛的关注并在各个领域广泛应用,系统介绍基于支持向量机的网络安全风险评估,给出其可行性、优越性及SVM评估模型,最后提出该研究发展方向与前景的预见。
Statistical Learning Theory (SLT) is a theory that specializes in machine learning rules in the case of small samples. It is used as a concrete realization algorithm of the VC dimension theory and the Structure Risk Minimization (SRM) principle of statistical learning theory Support vector machin (SVM), set optimization, kernel and best popularization ability, etc. Its outstanding learning ability is widely concerned and widely used in all fields. The introduction of the system based on Support Vector Machine , Gives its feasibility, superiority and SVM evaluation model, and finally puts forward the prospect of this research direction and prospect.