以The 2007 International Conference of Data Mining and Knowledge Engineering为例，怎么查找editor？到该会议主页，contact us->Editorial board->Editors

         总而言之，一个会议的网页上应该包含了你所需要的所有信息，诸如Editors之类。

         但是NIPS 2004 没找到

          要求添加Semi-supervised learning using Gaussian fields and harmonic functions.这篇论文的：the editors, the publishing company, and the place of the publishing company in references of conference proceedings.解决方案：到谷歌上搜索www.informatik.uni-trier.de    icml  2003   就能找到。
         多日想不出此问题的解决方案，经过shuling wang老师提醒，终结解决方案：到web of science上搜，editors和publisher这些信息均能找到，如NIPS上论文learning with local and global consistency和Feature extraction from tumor gene expression profiles using DCT and DFT均能找到

本课件帮助科研人员在LaTeX文本编辑环境下如何利用EndNote软件编辑参考文献。

EndNote在LaTeX中的运用   （Understand completely）

BibTeX Export_Sww.ens.     
首次：在Endnote菜单中选择Edit->Output sytle-> Open style manger->  选中BibTex export；以后：只要Edit->Output sytle-> BibTex export ，则Endnote中的文献以Latex的方式呈现

 在Endnote工具栏中有搜索按钮，搜索对应论文的标题即可

关于四分位数(Quartile)在wikipedia维基百科 上搜索英语版或者中文版都有很清晰的解释

在 wikipedia维基百科 上搜索 Box Plot ：

箱形图（Box-plot）又称为盒须图、盒式图或箱线图，是一种用作显示一组数据分散情况资料的统计图。因型状如箱子而得名。在各种领域也经常被使用，常见于品质管理。不过作法相对较较繁琐。
箱形图于1977年由美国著名统计学家 John Tukey发明。它能显示出一组数据的最大值、最少值、中位数、下四分位数及上四分位数。

以下是箱形图的具体例子：

                            +-----+-+      

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

                            +-----+-+   

+---+---+---+---+---+---+---+---+---+---+   數線

0   1   2   3   4   5   6   7   8   9  10

这组数据显示出：

• 最小值(min)=5。
• 下四分位数(Q1)=7。
• 中位数(Med)=8.5。
• 上四分位数(Q3)=9。
• 最大值(max)=10。
• 平均值=8。
• 四分位间距(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) %产生矩阵X的每一列的盒图和“须”图，“须”是从盒的尾部延伸出来，并表示盒外数据长度的线，如果“须”的外面没有数据，则在“须”的底部有一个点。
boxplot(X,notch) %当notch=1时，产生一凹盒图，notch=0时产生一矩箱图。
boxplot(X,notch,'sym') %sym表示图形符号，默认值为“+”。
boxplot(X,notch,'sym',vert) %当vert=0时，生成水平盒图，vert=1时，生成竖直盒图（默认值vert=1）。
boxplot(X,notch,'sym',vert,whis) %whis定义“须”图的长度，默认值为1.5，若whis=0则boxplot函数通过绘制sym符号图来显示盒外的所有数据值。

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)

版权声明：转载时请以超链接形式标明文章原始出处和作者信息及本声明
http://netessays.blogbus.com/logs/30974661.html

Endnote里的文献类型有很多，有journal article、conference paper等。其中，关于会议的文献类型有conference proceeding和conference paper两个，这两
个有什么区别呢？

Endnote官方网站上有如下描述：

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.

也就是说，对于一般已经出版的proceeding，应该归结到Conference Paper里面，只有没有出版的Proceeding才放到conference proceeding。



 参考文献样式：
（1）       LNAI，LNCS是杂志，会议，会议录？怎么写参考文献格式，按照什么格式Hongqiang Wang 师兄主页书中的章还是Chunhou Zheng 师兄主页第六和第八个参考文献。如Feature extraction from tumor gene expression profiles using DCT and DFT到web of science上可以查到是个会议论文，会议名字也能查到。在Book Series: LECTURE NOTES IN ARTIFICIAL INTELLIGENCE中，Shuling Wang老师讲应该按照会议论文格式   
参考文献样式：
比较好的方式：作者，“文章名，”杂志或会议名，卷号（会议地点），期号（会议时间），页码，年份。（参见March 10，2010邮件）
(2)Riemannian manifold learning: 查老师这篇文章的第48篇引用文献是来自维基百科，51和52是引用的数据，值得学习这种引用方式！