| 1. | Syntactic , semantic and pragmatic analyses of quot
英语情态动词的语义分类及语用分析 |
| 2. | Algorithm of feature selection for semantic video classifier
视频语义分类特征选择算法 |
| 3. | Image semantic classification by using svm
基于支持向量机的图像语义分类 |
| 4. | Semantic categorization task
语义分类作业方法 |
| 5. | A new approach to the classification of turning complex sentences by semantic relationship
转折复句语义分类的新尝试栽 |
| 6. | Secondly , the lexical - semantic theory into agriculture field was introduced , and the classifying system by agriculture word ' s semantic was attempted to build
然后把词汇语义理论引入到农业专业领域，从语义的角度对农业词语进行统计分析，尝试性地建立了农业词语语义分类体系。 |
| 7. | Experimental results show that the proposed approach is more accurate in image semantic classification than other ones , such as svms classifier using color and textural features
实验结果表明，本文提出的方法在图像语义分类的准确性方面要优于诸如采用色彩特征和纹理特征的支持向量机分类器的其它方法。 |
| 8. | This paper analyzed the strong relationship between color features and human sensations and showed how high - level emotional representation of painting can be inferred form perceptual level features suited for the particular classes ( elegance vs . flaring )
分析了服装图像的颜色特征与情感之间的相关性,采用概率神经网络作为分类算法来完成情感语义分类。 |
| 9. | 3 ) semantic classification model based som network we use the classification model to combines attributes within a database . this is done using an unsupervised learning algorithm . the output is used as training data for the next stage
3 )基于som网络的语义分类模型设计建立som网络模型，将元数据特征向量进行分类，形成bp网络的目标向量，用于匹配规则的提取。 |
| 10. | Finally , the thesis probed how to obtain the agriculture words and related knowledge from agriculture knowledge texts by auto - extracting and artificial way and build the agriculture word knowledge database based on semantic classifying
最后采用自动抽词与人工过滤相结合的方式，从农业知识文本知识库中获取农业词语及其相关知识，建立了一个基于语义分类的农业词语知识库。 |
