基于支持向量机和转换的错误驱动学习方法的组块识别  

SVM-based chunk recognition and transformation-based error-driver learning

作　　者：王达[1] 张坤[1] 

机构地区：[1]南阳师范学院计算机与信息技术学院,河南南阳473061

出　　处：《南阳师范学院学报》2009年第6期68-70,共3页Journal of Nanyang Normal University

摘　　要：支持向量机在高维特征空间的输入数据上具有较高的泛化性能,能够独立于小范围内的数据维数计算.基于转换的学习方法能自动融合不同类型的知识,所得到的模板可以显示一些语言知识,这些语言知识对于语言学及其他相关研究有重要意义.利用支持向量机和基于转换的错误驱动学习相结合,能够达到较为满意的组块识别效果.The paper presents a method of Chinese chunk recognition based on Support Vector Machines （SVM） and transformation-based error-driven learning. It is well known that SVM is good at achieving high generalization of very high dimensional feature space. SVM can be trained in a high dimensional space with smaller computational cost independent of their dimensionalities. Transformation-based learning method can combine many kinds features and express much knowledge of linguistic which is very important to other research. Using SVM method and Trans- formation-based learning method can combine the advantages and get a satisfaction of identification.

关 键 词：组块 支持向量机 基于转换的学习 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]