| 基于支持向量机和转换的错误驱动学习方法的组块识别 |
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| 引用本文： | 王达,张坤.基于支持向量机和转换的错误驱动学习方法的组块识别[J].南阳师范学院学报,2009,8(6):68-70. |
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| 作者姓名： | 王达 张坤 |
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| 作者单位： | 南阳师范学院计算机与信息技术学院,河南南阳,473061 |
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| 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.
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| 关 键 词： | 组块 支持向量机 基于转换的学习 |
SVM-based chunk recognition and transformation-based error-driver learning |
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| WANG Da,ZHANG Kun.SVM-based chunk recognition and transformation-based error-driver learning[J].Journal of Nanyang Teachers College,2009,8(6):68-70. |
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| Authors: | WANG Da ZHANG Kun |
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| Institution: | School of Computer and Information Technology;Nanyang Normal University;Nanyang 473061;China |
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| Abstract: | 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 r... |
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| Keywords: | chunking support vector machine transformation-based learning |
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