基于正反馈的支持向量机  

Support Vector Machines Based on Positive Feedback

作　　者：杨强[1] 吴中福[1] 余平[1] 钟将[1] 

机构地区：[1]重庆大学计算机学院,重庆400030

出　　处：《重庆大学学报（自然科学版）》2004年第5期41-44,共4页Journal of Chongqing University

摘　　要：在分析现有的基于高斯核的支持向量机(包括基于K-邻域法的支持向量机)的优缺点的基础上,通过对支持向量机之所以能够描述数据集的分布特征的本质进行分析,突破目前在构造支持向量机中存在的"所有支持向量与样本之间的在特征空间中的内积所对应的核函数参数一定要相等"的这一苛刻要求,提出了用于模式识别的基于正反馈的支持向量机。给出了基于正反馈的支持向量机的算法。通过对人工数据和现实数据的仿真实验,表明基于正反馈的支持向量机在推广性能方面明显优于现有的支持向量机。Support vector machines based on positive feedback are put forward with the analysis of both advantages and disadvantages of current support vector machines based on Gaussian kernel function(including support vector machines based on K-nearest neighbors) and the essence why support vector machines is capable of describing data sets'distribution characteristics, thus the rigor constraint is overcome that maintains 'corresponding parameters ofkernel function support vectors should be equal'. The learning algorithm of support vector machines based on positive feedback is given. Simulation experiments of artificialand real data proves that support vector machines based on positive feedback is be obviously superior to current ones in its generation capabilities. Though, only the support vector machines based on positive feedback for pattern recognition is discussed, the idea included in support vector machines based on positive feedback is using kernel functions with different corresponding parameters to construct support vector machines, and is adaptive to other types support vector machines.

关 键 词：径向基神经网络 K-邻域法 核函数 支持向量机 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]