以The 2007 International Conference of Data Mining and Knowledge Engineering為例，怎么查找editor？到該會(huì)議主頁，contact us->Editorial board->Editors

         總而言之，一個(gè)會(huì)議的網(wǎng)頁上應(yīng)該包含了你所需要的所有信息，諸如Editors之類。

         但是NIPS 2004 沒找到

          要求添加Semi-supervised learning using Gaussian fields and harmonic functions.這篇論文的：the editors, the publishing company, and the place of the publishing company in references of conference proceedings.解決方案：到谷歌上搜索www.informatik.uni-trier.de    icml  2003   就能找到。
         多日想不出此問題的解決方案，經(jīng)過shuling wang老師提醒，終結(jié)解決方案：到web of science上搜，editors和publisher這些信息均能找到，如NIPS上論文learning with local and global consistency和Feature extraction from tumor gene expression profiles using DCT and DFT均能找到

本課件幫助科研人員在LaTeX文本編輯環(huán)境下如何利用EndNote軟件編輯參考文獻(xiàn)。

EndNote在LaTeX中的運(yùn)用   （Understand completely）

BibTeX Export_Sww.ens.     
首次：在Endnote菜單中選擇Edit->Output sytle-> Open style manger->  選中BibTex export；以后：只要Edit->Output sytle-> BibTex export ，則Endnote中的文獻(xiàn)以Latex的方式呈現(xiàn)

 在Endnote工具欄中有搜索按鈕，搜索對(duì)應(yīng)論文的標(biāo)題即可

關(guān)于四分位數(shù)(Quartile)在wikipedia維基百科 上搜索英語版或者中文版都有很清晰的解釋

在 wikipedia維基百科 上搜索 Box Plot ：

箱形圖（Box-plot）又稱為盒須圖、盒式圖或箱線圖，是一種用作顯示一組數(shù)據(jù)分散情況資料的統(tǒng)計(jì)圖。因型狀如箱子而得名。在各種領(lǐng)域也經(jīng)常被使用，常見于品質(zhì)管理。不過作法相對(duì)較較繁瑣。
箱形圖于1977年由美國著名統(tǒng)計(jì)學(xué)家 John Tukey發(fā)明。它能顯示出一組數(shù)據(jù)的最大值、最少值、中位數(shù)、下四分位數(shù)及上四分位數(shù)。

以下是箱形圖的具體例子：

                            +-----+-+      

  *           o     |-------|   + | |---|

                            +-----+-+   

+---+---+---+---+---+---+---+---+---+---+   數(shù)線

0   1   2   3   4   5   6   7   8   9  10

這組數(shù)據(jù)顯示出：

• 最小值(min)=5。
• 下四分位數(shù)(Q1)=7。
• 中位數(shù)(Med)=8.5。
• 上四分位數(shù)(Q3)=9。
• 最大值(max)=10。
• 平均值=8。
• 四分位間距(interquartile range)=Q3 − Q1=2

http://www.physics.csbsju.edu/stats/box2.html

The box plot (a.k.a. box and whisker diagram) is a standardized way of displaying the distribution of data based on the five number summary: minimum, first quartile, median, third quartile, and maximum. In the simplest box plot the central rectangle spans the first quartile to the third quartile (the interquartile range or IQR). A segment inside the rectangle shows the median and "whiskers" above and below the box show the locations of the minimum and maximum.

This simplest possible box plot displays the full range of variation (from min to max), the likely range of variation (the IQR), and a typical value (the median). Not uncommonly real datasets will display surprisingly high maximums or surprisingly low minimums called outliers. John Tukey has provided a precise definition for two types of outliers:

• Outliers are either 3×IQR or more above the third quartile or 3×IQR or more below the first quartile.
• Suspected outliers are are slightly more central versions of outliers: either 1.5×IQR or more above the third quartile or 1.5×IQR or more below the first quartile.

If either type of outlier is present the whisker on the appropriate side is taken to 1.5×IQR from the quartile (the "inner fence") rather than the max or min, and individual outlying data points are displayed as unfilled circles (for suspected outliers) or filled circles (for outliers). (The "outer fence" is 3×IQR from the quartile.)

If the data happens to be normally distributed,

IQR = 1.35 σ

where σ is the population standard deviation.

Suspected outliers are not uncommon in large normally distributed datasets (say more than 100 data-points). Outliers are expected in normally distributed datasets with more than about 10,000 data-points. Here is an example of 1000 normally distributed data displayed as a box plot:

Note that outliers are not necessarily "bad" data-points; indeed they may well be the most important, most information rich, part of the dataset. Under no circumstances should they be automatically removed from the dataset. Outliers may deserve special consideration: they may be the key to the phenomenon under study or the result of human blunders.

Example A

Consider two datasets:

A1={0.22, -0.87, -2.39, -1.79, 0.37, -1.54, 1.28, -0.31, -0.74, 1.72, 0.38, -0.17, -0.62, -1.10, 0.30, 0.15, 2.30, 0.19, -0.50, -0.09}

A2={-5.13, -2.19, -2.43, -3.83, 0.50, -3.25, 4.32, 1.63, 5.18, -0.43, 7.11, 4.87, -3.10, -5.81, 3.76, 6.31, 2.58, 0.07, 5.76, 3.50}

Notice that both datasets are approximately balanced around zero; evidently the mean in both cases is "near" zero. However there is substantially more variation in A2 which ranges approximately from -6 to 6 whereas A1 ranges approximately from -2½ to 2½.

Below find box plots and the more traditional error bar plots (with 1-σ bars). Notice the difference in scales: since the box plot is displaying the full range of variation, the y-range must be expanded.

Example B

B1={1.26, 0.34, 0.70, 1.75, 50.57, 1.55, 0.08, 0.42, 0.50, 3.20, 0.15, 0.49, 0.95, 0.24, 1.37, 0.17, 6.98, 0.10, 0.94, 0.38}

B2= {2.37, 2.16, 14.82, 1.73, 41.04, 0.23, 1.32, 2.91, 39.41, 0.11, 27.44, 4.51, 0.51, 4.50, 0.18, 14.68, 4.66, 1.30, 2.06, 1.19}

Notice that the datasets span much the same range of values (from about .1 to about 50) and that all the values are positive. Most of the B1 values are less than one whereas most of the B2 values are more than one. We can use a log scale to better display this large range of values:

On the other hand, a straightforward plot of the sample means and population standard deviations, suggests negative values (which prevents use of a log-scale) and broad overlap between the two distributions. (A t-test would suggest B1 and B2 are not significantly different.)

Example C

One case of particular concern --where a box plot can be deceptive-- is when the data are distributed into "two lumps" rather than the "one lump" cases we've considered so far.

A "bee swarm" plot shows that in this dataset there are lots of data near 10 and 15 but relatively few in between. See that a box plot would not give you any evidence of this.

格式 boxplot(X) %產(chǎn)生矩陣X的每一列的盒圖和“須”圖，“須”是從盒的尾部延伸出來，并表示盒外數(shù)據(jù)長度的線，如果“須”的外面沒有數(shù)據(jù)，則在“須”的底部有一個(gè)點(diǎn)。
boxplot(X,notch) %當(dāng)notch=1時(shí)，產(chǎn)生一凹盒圖，notch=0時(shí)產(chǎn)生一矩箱圖。
boxplot(X,notch,'sym') %sym表示圖形符號(hào)，默認(rèn)值為“+”。
boxplot(X,notch,'sym',vert) %當(dāng)vert=0時(shí)，生成水平盒圖，vert=1時(shí)，生成豎直盒圖（默認(rèn)值vert=1）。
boxplot(X,notch,'sym',vert,whis) %whis定義“須”圖的長度，默認(rèn)值為1.5，若whis=0則boxplot函數(shù)通過繪制sym符號(hào)圖來顯示盒外的所有數(shù)據(jù)值。

Examples 1

The following commands create a box plot of car mileage grouped by country.

load carsmall

boxplot(MPG,Origin)

Examples 2
The following example produces notched box plots for two groups of sample data.

x1 = normrnd(5,1,100,1);

x2 = normrnd(6,1,100,1);

boxplot([x1,x2],'notch','on')

 Examples 3

x1 = normrnd(5,1,100,1);
x2 = normrnd(6,1,100,1);
 boxplot([x1,x2])

 The difference between the medians of the two groups is approximately 1.Since the notches in the boxplot do not overlap, you can conclude, with 95% confidence, that the true medians do differ.

 Examples 4

The following figure shows the boxplot for same data with the length of the whiskers specified as 1.0 times the interquartile range. Points beyond the whiskers are displayed using +.

x1 = normrnd(5,1,100,1); x2 = normrnd(6,1,100,1); boxplot([x1,x2],'notch','on','whisker',1)

版權(quán)聲明：轉(zhuǎn)載時(shí)請(qǐng)以超鏈接形式標(biāo)明文章原始出處和作者信息及本聲明
http://netessays.blogbus.com/logs/30974661.html

Endnote里的文獻(xiàn)類型有很多，有journal article、conference paper等。其中，關(guān)于會(huì)議的文獻(xiàn)類型有conference proceeding和conference paper兩個(gè)，這兩
個(gè)有什么區(qū)別呢？

Endnote官方網(wǎng)站上有如下描述：

The Conference Proceedings reference type is best used for unpublished 
proceedings.  Articles that are published as part of the comprehensive 
conference proceedings should be entered as Conference Paper references.

也就是說，對(duì)于一般已經(jīng)出版的proceeding，應(yīng)該歸結(jié)到Conference Paper里面，只有沒有出版的Proceeding才放到conference proceeding。



 參考文獻(xiàn)樣式：
（1）       LNAI，LNCS是雜志，會(huì)議，會(huì)議錄？怎么寫參考文獻(xiàn)格式，按照什么格式Hongqiang Wang 師兄主頁書中的章還是Chunhou Zheng 師兄主頁第六和第八個(gè)參考文獻(xiàn)。如Feature extraction from tumor gene expression profiles using DCT and DFT到web of science上可以查到是個(gè)會(huì)議論文，會(huì)議名字也能查到。在Book Series: LECTURE NOTES IN ARTIFICIAL INTELLIGENCE中，Shuling Wang老師講應(yīng)該按照會(huì)議論文格式   
參考文獻(xiàn)樣式：
比較好的方式：作者，“文章名，”雜志或會(huì)議名，卷號(hào)（會(huì)議地點(diǎn)），期號(hào)（會(huì)議時(shí)間），頁碼，年份。（參見March 10，2010郵件）
(2)Riemannian manifold learning: 查老師這篇文章的第48篇引用文獻(xiàn)是來自維基百科，51和52是引用的數(shù)據(jù)，值得學(xué)習(xí)這種引用方式！