In our case, effective search results can be obtained by creating a vector for each letter in a word and returning results based on the closest match in vector space.
在我们的例子中，可以通过为词中的每个字母创建向量并返回向量空间中最接近的匹配结果来获得有效的搜索结果。
Vector instructions operate on vectors of scalar quantities and on scalar quantities, where the scalar size is byte, halfword, word, and quadword.
矢量指令作用于标量的矢量及标量，其中标量范围为字节、半字、字和四字。
Remember, this is a vector processor. So you are not actually comparing register 3 with the number 0. Instead, you are comparing each word of register 3 with the number 0.
请注意这是一个向量处理器，所以并非将寄存器3与数值0作实际对比，相反，所对比的是寄存器3的每一个字与数值0。
It also provides for byte, halfword, word, and quadword operand fetches and stores between storage and a set of 32 Vector Registers (VRs).
它还规定在存储设备与一组32位矢量寄存器(Vector Register,VR)之间存取和储存的字节、半字、字和四字操作数。
Traditional vector-space approaches will treat a word like harrington as having a single vector.
传统的向量空间方法将把诸如harrington之类的词作为一个向量来处理。
In addition, a text classification system based on Vector Space Model is studied and a new method for calculating word weight is proposed.
此外，本文还研究了基于向量空间模型的自动文本分类方法，提出了一个新的词权重计算方法，该方法有效提高了分类精度。
Research on the key techniques and typical methods of text categorization are being done, and the method of text categorization based on word vector space model is presented in the dissertation.
本文对文本分类的关键技术及典型分类方法进行了研究，提出基于词向量空间模型的文本分类方法。
Since word order similarity is the main factor to the ranking results, an algorithm based on vector distance is devised to compute word order similarity in this paper.
由于词序相似度是影响简拼搜索排序结果的主要因素，该文提出了基于向量距离计算词序相似度的算法。
So, the traditional KNN arithmetic, clusters training document with highly overlapping word is improved, central vector of cluster is gained.
为此，改进了传统KNN算法，将训练文本中相似度大的文本合并，称为一簇，并计算簇的中心向量。
To alleviate the blindness of Linde-Buzo-Gray algorithm(LBG algorithm) while generating initial codebook, a new code word splitting method based on covariance of training vector is proposed.
针对LBG算法中初始码书生成存在盲目性的问题，提出了一种基于训练向量集合分量之间的相关性进行码向量分割的方法。
Traditional method faces the difficulties that need to handle high dimension vector and Chinese word segment.
传统的中文文本聚类方法需要对高维向量进行处理，有对中文文本需要进行分词处理等困难。
Traditional method faces the difficulties that need to handle high dimension vector and Chinese word segment.
传统的中文文本聚类方法需要对高维向量进行处理，有对中文文本需要进行分词处理等困难。
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