毕业论文英文翻译原文

论文（Paper）或：dissertation（论文）或：thesis（论文）经常说的：）~Englishdissertation(英语论文)Graduationthesis(毕业论文)

你的邮箱发不进去，请换一个，这里发部分供你参考Principal component analysisPrincipal component analysis (PCA) is a mathematical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of uncorrelated variables called principal components. The number of principal components is less than or equal to the number of original variables. This transformation is defined in such a way that the first principal component has as high a variance as possible (that is, accounts for as much of the variability in the data as possible), and each succeeding component in turn has the highest variance possible under the constraint that it be orthogonal to (uncorrelated with) the preceding components. Principal components are guaranteed to be independent only if the data set is jointly normally distributed. PCA is sensitive to the relative scaling of the original variables. Depending on the field of application, it is also named the discrete Karhunen–Loève transform (KLT), the Hotelling transform or proper orthogonal decomposition (POD).PCA was invented in 1901 by Karl Pearson.[1] Now it is mostly used as a tool in exploratory data analysis and for making predictive models. PCA can be done by eigenvalue decomposition of a data covariance matrix or singular value decomposition of a data matrix, usually after mean centering the data for each attribute. The results of a PCA are usually discussed in terms of component scores (the transformed variable values corresponding to a particular case in the data) and loadings (the weight by which each standarized original variable should be multiplied to get the component score) (Shaw, 2003).PCA is the simplest of the true eigenvector-based multivariate analyses. Often, its operation can be thought of as revealing the internal structure of the data in a way which best explains the variance in the data. If a multivariate dataset is visualised as a set of coordinates in a high-dimensional data space (1 axis per variable), PCA can supply the user with a lower-dimensional picture, a "shadow" of this object when viewed from its (in some sense) most informative viewpoint. This is done by using only the first few principal components so that the dimensionality of the transformed data is is closely related to factor analysis; indeed, some statistical packages (such as Stata) deliberately conflate the two techniques. True factor analysis makes different assumptions about the underlying structure and solves eigenvectors of a slightly different matrix.

毕业论文的英文翻译是thesis，音标是英 [ˈθi:sɪs]   美 [ˈθisɪs]  。

n.论文，毕业论文;论点，论题;命题

1、There is no empirical evidence to support his thesis.

他的论文缺乏实验证据的支持。

2、How well does this thesis stand up to close examination?

这个命题经得起推敲吗？

3、He has finished his thesis.

他的论文完成了。

4、She's finished writing her thesis.

她那篇论文写出来了。

5、Please write an abstract of this article 〔 thesis 〕.

请写一份这本书〔这篇论文〕的摘要。

The article has a clear-cut thesis and arguments, but lacks reasoning.

文章论点、论据鲜明，但缺乏论证。

A Thesis Submitted as a Partial Fulfillment of the Requirement for the Degree of B. A./B. S. in ***这是标准的学士学位毕业论文的说法，.代表文学学士，.代表理学学士，***处填上专业。

“毕业论文”的英文：Graduation Dissertation

Dissertation 读法 英 [,dɪsə'teɪʃ(ə)n]  美 ['dɪsɚ'teʃən]

n. 论文，专题；学术演讲

短语：

1、academic dissertation 学位论文 ; 学术论文

2、Graduation Dissertation 毕业论文

3、Doctorate dissertation 博士论文

4、Dissertation Committee 论文委员会

5、dissertation topics 毕业论文题目

词义辨析：

article, paper,dissertation, essay, prose, thesis这组词都有“文章”的意思，其区别是：

1、article 多指在报刊、杂志上发表的非文艺性的文章，包括新闻报导、学术论文等。

2、paper 正式用词，多指在学术刊物上发表或在学术会议上宣读的专题论文，也指高等学校的学期论文，或学校里的作文练习。

3、dissertation 书面语用词，指独立研究后所写的较为详细的专题文章；也可指学位论文。

4、essay 指任何一种非小说性的，篇幅不长、结构简练的文章，如论说文、报道、评论、讽刺性杂文等。

5、prose 专指散文。

6、thesis 既可指毕业论文、学位论文，又可指一般的为阐述学术观点而写的论文。

例句：

1、Exploring "Trinity Working Mode" of Integrating Graduation Field Work, Graduation Dissertation and Employment on Graduation.

毕业实习、毕业论文与学生就业三位一体工作模式探索。

2、On Problems in Writing Graduation Dissertation

关于撰写毕业论文应该注意的问题。