Abstract: This paper proposes a new parameterless support vector machine classifier based on quadratic programming, which avoids some shortcomings such as the need for choosing regularized parameter in standard SVM. Its formulation is simple and easy to be realized. Some numerical results illustrate that the new SVM classifier is feasible and effective.

Key words: pattern recognition, support vector machine(SVM), quadratic programming

摘要： 提出了一种新的基于二次规划的无参数支持向量机分类模型，克服了标准支持向量机需要选择正则化参数的缺点，而且该模型简单，易于实现。数值实验表明了该模型的可行性和有效性。

关键词: 模式识别, 支持向量机, 二次规划

CLC Number: 

• TP181