强行停止matlab的方法

1.从任务管理器中关掉matlab.关闭整个matlab,无法保存以前计算的结果.

2.先激活command window,然后按ctrl+c.这方法有个好处就是能保存你以前计算的结果.

Latex 中常遇到的一些问题

1.如何显示特殊符号

符號	作用	文稿上使用
\	下排版命令	$\backslash$
％	註解	\％
#	定義巨集	\#
~	產生一個空白	\~{}
$	進入（離開）數學模式	\$
{	標示命令的作用範圍	\{
}	標示命令的作用範圍	\}
&	表格中的分隔符號	\&

2 .如何插图

\vskip3mm {\centering

\vbox{\centerline{\psfig{figure=czxufsvm.eps,width=8cm}} \vskip2mm

{\small图(8)\quad 基于\textit{CZX-IFSVM}决策面.

}}

}

说明:图片的标题在图正下方.
3.如何制作表格

制作表格的链接地址:Latex 表格制作

下面是我的一些使用心得:
注意:当latex的格式为两栏,而表格要占用整个通栏时,用下面红色代码:
注意在论文中另外会有下面两行代码:

\onecolumn\begin{multicols}{2}%在第一页的结束出现

\end{multicols} %在文章的最后出现

如果没有,下面的代码会出现错误.

以下的表格为三线表格.

\end{multicols}

\vbox{\centering{

\begin{center}

\begin{tabular}{@{}cccccc@{}}\toprule

数据集合 & CSVM & FSVM & ZX-FSVM & CZX-FSVM & CZX-ISVM \\ \midrule

pid & 75.98\% & - & 78.33\% & \textbf{79.11\%} & \textbf{79.33\%} \\

sonar & 76.92\% & - & 77.89\% & \textbf{80.77\%} & \textbf{80.77\%} \\

monk3 & 95.14\% & - & 96.76\% & \textbf{97.00\% } & \textbf{97.69\%} \\

crt & 85.76\% & - & 85.76\% & \textbf{87.50\%} & \textbf{87.80\%} \\

wdbc & 97.19\% & - & 97.54\% & 96.49\% & \textbf{97.54\%} \\ \bottomrule

\end{tabular}

\vskip2mm

\small 图9\quad 5中svm实验结果比较

\end{center}

}} \vskip3mm

\begin{multicols}{2}%开始双栏显示.

4.公式左对齐问题

在网上找了很多结果发现都不好使,结果在书上看到了一种方法,还是比较好用的.代码如下

\begin{eqnarray}

\lefteqn{\underset{\beta}{\textit{min} }

\sum_{i=1}^{l^{'}}\beta_{i}(x_{i}\cdot

x_{i})- \sum_{i=1,j=1}^{l^{'}}\beta_{i}\beta_{j}(x_{i}\cdot x_{j});} \hspace{70mm} \\

\lefteqn{ st:\sum_{i=1}^{l^{'}}\beta_{i}=1;} \hspace{50mm}\\

\lefteqn{ 0\leqslant \beta_{i} \leqslant

D,i=1,\cdots,l^{'};}\hspace{50mm}

\end{eqnarray}

semi-supervised learning(转载)

转自(http://www.shen-wei.cn/BBSXP/ShowPost.asp?ThreadID=1029)

以下为Xiaojin Zhu在ICML2007上的陈述内容梗概，更多内容可以在他的主页上得到。懒得整理成中文了，而且很多术语虽然理解了意思，但还不知道确切的中文描 述...现在才深刻体会到，要想学习前沿的知识，只有通过互联网，平常看书只是看些稳定成型的老知识。。。

半监督学习已经兴起七八年了吧，但在中国还是刚刚起步罢。

一、Introduction to semi-supervised learning

What is semi-supervised learning and transductive learning? Why can we ever learn a classifier from unlabeled data? Does unlabeled data always help? Which semi-supervised learning methods are out there? Which one should I use? Answers to these questions set the stage for a detailed look at individual algorithms.

二、Semi-supervised learning algorithms

In fact we will focus on classification algorithms that uses both labeled and unlabeled data. Several families of algorithms will be discussed, which uses different model assumptions:

1、Self-training

Probably the earliest semi-supervised learning method. Still extensively used in the natural language processing community.

2、Generative models

Mixture of Gaussian or multinomial distributions, Hidden Markov Models, and pretty much any generative model can do semi-supervised learning. We will also look into the EM algorithm, which is often used for training generative models when there is unlabeled data.

3、S3VMs

Originally called Transductive SVMs, they are now called Semi-Supervised SVMs to emphasize the fact that they are capable of induction too, not just transduction. The idea is simple and elegant, to find a decision boundary in 'low density' regions. However, the optimization problem behind it is difficult, and so we will discuss the various optimization techniques for S3VM, including the one used in SVM-light, Convex-Concave Procedure (CCCP), Branch-and-Bound, continuation method, etc.

4、Graph-based methods

Here one constructs a graph over the labeled and unlabeled examples, and assumes that two strongly-connected examples tend to have the same label. The graph Laplacian matrix is a central quantity. We will discuss representative algorithms, including manifold regularization.

5、Multiview learning

Exemplified by the Co-Training algorithm, these methods employ multiple 'views' of the same problem, and require that different views produce similar classifications.

6、Other approaches

Metric based model selection, tree-based learning, information-based method, etc.

7、Related problems

Regression with unlabeled data, clustering with side information, classification with positive and unlabeled data; dimensionality reduction with side information, inferring label missing mechanism, etc.

三、Semi-supervised learning in nature

Long before computers come around and machine learning becomes a discipline, learning has occurred in nature. Is semi-supervised learning part of it? The research in this area has just begun. We will look at a few case studies, ranging from infant word learning, human visual system, and human categorization behavior.

四、Challenges for the future

There are many open questions. What new algorithms / assumptions can we make? How to efficiently perform semi-supervised learning for very large problems? What special methods are needed for structured output domains? Can we find a way to guarantee that unlabeled data would not decrease performance? What can we borrow from natural learning? We suggest these as a few potential research directions.

研究半监督的人，主页上有更多更详细的介绍：

http://pages.cs.wisc.edu/~jerryzhu/

http://www.kyb.tuebingen.mpg.de/~chapelle

International Conference on Machine Learning (2007,2008)

International Conference on Machine Learning

(one of the best conference on machine learning )
the links:

ICML2008

ICML2007

高等应用数学问题的MATLAB求解

哈哈,这本书感觉不错!收藏啦

二维正态分布随机数产生命令:

Rn=mvnrnd([0,1],[2,1;10,2],2000);
[0,1]表示均值;
[2,1;10,2]表示协方差矩阵;
2000:表示要求产生的随机数个数;

Link:高等应用数学问题的MATLAB求解