| 摘要: |
| 支持向量机的关键在于获取分离超平面，先用感知机的迭代算法获取初始分离超平面，然后将初始分离超平面不断地旋转和平移，直至几何间隔达到最大且完全分离训练数据集，此时的分离超平面就近似支持向量机的分离超平面，分类效果最好，并使用分类数据进行检验，说明此方法有效。 |
| 关键词: 支持向量机 分离超平面 分类算法 凸二次规划 机器学习 |
| DOI： |
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| An Algorithm for Separating Hyperplane Based on Support Vector Machine |
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YI Xiao shi1, LIU Nian2
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| Abstract: |
| The key of support vector machine is to obtain a separating hyperplane, firstly to receive the initial separation hyperplane by iterative algorithm of perceptron, then to continuously rotate and translate the initial separation hyperplane, until a geometric interval up to the maximum and the complete separation of the training data set, at this moment, the separation hyperplane is approximately separation hyperplane of support vector machine, and the classification effect is the best. After the test by classification data, the result shows that this method is effective. |
| Key words: support vector machine separation hyperplane classification algorithm convex quadratic programming machine learning |
