Chinese translation for "feature matrix"

特征矩阵

Related Translations:

automatic feature:  自动特色

compatibility feature:  兼容性特性

infantile feature:  幼年特征

typical feature:  典型要素

ugly features:  丑恶面目丑恶嘴脸

software feature:  软件功能

roentgen feature:  x线征象

outstanding feature:  特角突出特点显著特征

wikipedia features:  维基百科特性

minimization feature:  最简化特征最简化特徵

Example Sentences:

	1.	A handwritten chinese character classifying algorithm is designed based on the character feature matrix with the excellent classifying effect 应用字符的特征矩阵设计了一个手写体汉字的分类识别算法，取得了较好的效果。
	2.	The feature matrix may be formed based on the character sub - strokes , including the sub - stroke length , position and direction information and so on 由字符的子笔画生成字符的特征矩阵，特征矩阵包含子笔画的长度、位置、方向等信息。
	3.	William s - y . wang has discussed in his paper the mathematical basis of the tree model popular in phylogenetic classification of languages and dialects and in biological sciences . his exposition on feature matrix to tree conversion is particularly enlightening 王士元在他的文章中讨论了在方言和语言演进研究以及生命科学里经常使用的树模型数学基础，并特别强调了矩阵到树的转换。
	4.	First we construct a covariance matrix from sample images , then compute the eigenvalues and corresponding eigenvectors of the covariance matrix , construct a feature matrix with the eigenvectors . then every images in database can be projected into the feature matrix and gain a projection vector , so does the input image . then we can judge the resemblance between input image with each image in database by computing the distance between their projection vectors 我们首先根据采集的样本图像构造一个协方差矩阵，然后求取该矩阵的特征值，以这些矩阵特征值对应的特征向量构造出一个特征空间，然后将输入图像向该特征空间映射，将获取的映射系数与样本库中图像的映射系数进行距离计算，根据计算出的距离判定输入图像与样本图像间的匹配程度。
	5.	In this dissertation , we use a feature matrix and a semantic relevance matrix which is established by long - term learning the log of the feedback offered by users , then optimize the semantic relevance matrix , and finally , combine the lower - feature matrix and semantic relevance matrix to retrieve images . this approach achieves the estimation of the similarity between 对于某些特殊情况,仅仅依靠修改特征相似度不能起到很明显的效果,由此本文引入了语义关系矩阵,先通过对反馈日志的长期学习建立语义关系矩阵,之后再对语义关系矩阵进行优化,实现了同时被标注为负反馈的图像之间相似度的估计。
	6.	First we construct a covariance matrix from sample images , then compute the eigenvalues and corresponding eigenvectors of the covariance matrix , construct a feature matrix with the eigenvectors . then every image in database can be projected into the feature matrix and gain a projection vector , so does the input image . then we can judge the resemblance between input image with each image in database by computing the distance between their projection vectors 然后，根据采集的样本图像构造一个协方差矩阵，求取该矩阵的特征值，以这些矩阵特征值对应的特征向量构造一个特征空间，将输入图像向该特征空间映射，计算获取的映射系数与样本库中各类图像的映射系数的欧基里德距离，根据计算出的距离判定输入图像与样本图像间的匹配程度。

Similar Words：

"feature length films" Chinese translation, "feature list" Chinese translation, "feature list theory" Chinese translation, "feature marked" Chinese translation, "feature matching model" Chinese translation, "feature name" Chinese translation, "feature norm" Chinese translation, "feature noun" Chinese translation, "feature number" Chinese translation, "feature of bridge" Chinese translation