大数据中边界向量调节熵函数支持向量机研究  

Support Vector Machine Based on Boundary Vectors Adjustable Entropy Function

作　　者：林蔚[1] 刘婷[1] 吕伟国 

机构地区：[1]哈尔滨工程大学理学院,黑龙江哈尔滨150001

出　　处：《微电子学与计算机》2016年第8期149-152,157,共5页Microelectronics & Computer

基　　金：中央高校基本科研业务费专项基金资助项目(HEVCF20111119);黑龙江省博士后基金资助项目(3236301158))

摘　　要：当训练集的规模很大时,一般的支持向量机的学习过程需要占用大量的内存,寻优速度缓慢,不利于实际应用.提出了一种预抽取支持向量的支持向量机调节熵函数法.首先,利用两凸包相对边界向量方法预抽取出边界向量;然后,利用支持向量机调节熵函数法来训练预抽取的边界向量.实验表明,采用这种方法来训练样本集不仅降低了学习的代价,还提高了分类速度.When the size of the training set is large, learning process in general support vector machines take a lot of memory, optimizing slow, is not conducive to practical application. This paper presents a boundary vector-based SVM adjustable entropy function method. First, we use two methods convex hull boundary vectors relative pre- extracted boundary vectors; Then, to train pre-drawn boundary vectors in SVM adjustable entropy method. Experiments show that this method not only reduces the training sample set the price of learning, but also improve the classification rate.

关 键 词：数据挖掘 支持向量机 调节熵函数 预抽取支持向量 

分 类 号：TP31[自动化与计算机技术—计算机软件与理论]