The thesis presents a semantic vector algorithm, builds up the network of image semantic keywords, and realizes the composite retrieval of the image low-level feature and semantics characteristics.
提出了一种语义向量算法，构建了图像语义关键词网络，实现了图像底层视觉特征和语义的复合索引。
A multiclass text categorization model based on latent semantic analysis and support vector machine is researched and designed to enhance the accuracy of categorization.
为了提高文本分类的准确性，研究并设计了一个基于潜在语义分析和支持向量机的多类文本分类模型。
Using latent semantic analysis to extract feature, the affect of synonymy and polysemy in text representation process is eliminated and the dimension of text vector is reduced.
利用潜在语义分析进行特征抽取，消除多义词和同义词在文本表示时造成的偏差，并实现文本向量的降维。
Computing the semantic similarity of sentences by the method of cosine, eigenvalue come from the skeleton semantic clip, and the semantics of sentence expressed the vector space model.
用骨架语片做特征项，用空间向量模型表示文本语义，用语片的出现频度做语片权重，用余弦法计算文本间语义相似度。
The proposed method was based on vector support machine of semantic space in which text and user profile were represented by the semantic space.
提出基于语义空间的支持向量机的文本过滤，用语义来表示文本和用户模板。
Therefore, by some analysis, the combination of classification based on vector space model and which based on semantic is one of the best solutions to this problem.
本文通过认真分析，认为在基于向量空间模型的分类方法中可以适当地借鉴基于语义的分类方法中的权重设置方法。
This paper proposes a new Support Vector Machine(SVM) for anomaly intrusion detection method based on Latent Semantic Indexing(LSI).
论文提出了一种基于潜在语义索引（LSI）和支持向量机（SVM）的异常入侵检测方法。
This paper proposes a new Support Vector Machine(SVM) for anomaly intrusion detection method based on Latent Semantic Indexing(LSI).
论文提出了一种基于潜在语义索引（LSI）和支持向量机（SVM）的异常入侵检测方法。
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