基于概率测度支持向量机的静态手写数字识别方法  

Off-line Handwritten Digit Recognition by SVM Based on Probability Measure

作　　者：方向[1] 陈思佳[1] 贾颖[1] 

机构地区：[1]山东工商学院计算机部,烟台264005

出　　处：《微电子学与计算机》2015年第4期107-110,共4页Microelectronics & Computer

基　　金：山东省科技发展计划资助项目(2014GGX101044)

摘　　要：提出了基于概率测度的支持向量机算法,它采用概率分布作为均值嵌入构造再生希尔伯特空间,为了能够直接采用任何标准的基于核的学习技术,又构造了支持向量机的一般形式,称为基于概率测度的支持向量机(PMSVM).通过在MNIST数据库构建的虚拟样本进行实验,证明了该算法在识别率和时间消耗上更为有效.This paper proposes a support vector machine(SVM)algorithm based on probability measure.It employs these probability distributions as embeddings to reproduce kernel Hilbert space(RKHS).In order to reuse many standard kernel-based learning techniques in straightforward fashion,we construct the general form of support vector machine(SVM)called support vector machine based on probability measure(PM-SVM).We set up a virtual database by invariant transformation of the image through the MNIST database,and apply our algorithem to this database,The experimental results demonstrate the effectiveness of this algorithem in rate and efficiency to handwritten digit recognition.

关 键 词：静态手写数字识别 支持向量机 统计学习理论 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]