支持向量网络在MT资料模式识别中的应用

Application of support vector network in the pattern recognition of MT data

研究了有监督学习支持向量网络的的模式识别 ;在统计学习理论的基础上 ,研究了小样本、非线性高维模式识别 ;研究了有监督学习支持向量网络的非线性分类设计 ;设计了非线性优化问题的混合解法。针对油气识别预测的具体问题 ,对特征参数的提取和内积函数的选择进行了深入的研究。该方法能够较好地克服神经网络欠学习或过学习的弊端 ,应用于实际的MT资料 。

Pattern recognition of support vector network with supervising learning is studied in this paper. On the basis of statistical learning theory, we studied high-dimension nonlinear pattern recognition with small specimen and design of nonlinear classification of support vector network with supervised learning. A hybrid algorithm for solving nonlinear optimization problem is constructed. To deal with the classification of oil and gas, we studied deeply into the extraction of feature parameters and selection of inner product function. The proposed method can satisfactorily overcome the drawbacks of under or over learning problems of neural network. Desired results have been reached by applying the method to MT data.