Abstract: With combination of advantages of both Fisher discriminant analysis and support vector machines，this paper develops an improved classification algorithm，called Fisher-Support Vector Classifier.The central idea is that the vector [w?] of the optimal hyperplane is found along which the samples are projected such that the margin is maximized while within-class scatter is kept as small as possible.In linear case，it can be converted to traditional Support Vector Machines（SVM） to solve and doesn’t need to design new algorithms.In nonlinear case，a new algorithm is produced by the reproducing kernel theory.The test result shows that the Fisher-Support Vector Classifier established has a high accuracy and reliability.

摘要： 结合Fisher判别分析和支持向量机的优点，提出了一种新的分类算法—Fisher-SV分类器（简称FSVC）。该分类器的核心思想就是寻找最优分类面的法向量w*，使得样本向量在w*上做投影后，不仅使分类间隔达到最大，而且使类内离散程度尽可能小。对于线性情况，可以转化为传统的支持向量机求解，而不需要设计新的求解算法。对于非线性情况，利用再生核理论得出新的求解算法。实验结果表明，该分类器具有很高的准确度和可靠性。