基于SVM快速增量算法的HACCP控制点分类  

Fast Incremental SVM Method for Classification of CCPs on HACCP Implementation

作　　者：赵春江[1,2] 王开义[1,2] 喻钢[2] 徐红敏[3] 

机构地区：[1]北京工业大学计算机学院,北京100022 [2]国家农业信息化工程技术研究中心,北京100097 [3]北京石油化工学院数理系,北京102617

出　　处：《农业机械学报》2009年第S1期260-262,共3页Transactions of the Chinese Society for Agricultural Machinery

基　　金：国家"863"高技术研究发展计划资助项目(2008AA100803);"十一五"国家科技支撑计划资助项目(2006BAD10A04);北京市自然科学基金资助项目(4082012)

摘　　要：支持向量机方法已经在HACCP体系关键控制点的智能发现中取得较为满意的结果,但是在样本不断增加的情况下分类效率不高。本文采用支持向量机增量学习算法,优先选择可能成为支持向量的边界向量,减少参与训练的样本数量,进而实现增量学习。实验证明,改进的支持向量机增量算法在保证分类精度的同时,显著提高了分类速度。VM has already shown its successful application on the CCP discovery for HACCP implementation.However,the classic non-incremental SVM method is not an effective algorithm due to the thoroughly re-study for those samples are gradually added.We propose a new incremental algorithm.It makes use of the heuristic mode so that training can be firstly applied on the cases with greater possibilities to be SVs,and the training set can be reduced.The experiments show that the training speed is visibly improved without losing the precision of the classification.

关 键 词：危害分析与关键控制点 支持向量机 增量学习 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]