亚洲欧美第一页_禁久久精品乱码_粉嫩av一区二区三区免费野_久草精品视频

? 歡迎來到蟲蟲下載站! | ?? 資源下載 ?? 資源專輯 ?? 關于我們
? 蟲蟲下載站

?? faq.html

?? 數據挖掘分類方面的lib-svm經典算法的C++實現
?? HTML
?? 第 1 頁 / 共 4 頁
字號:
<html>
<head>
<title>LIBSVM FAQ</title>
</head>
<body bgcolor="#ffffcc">

<a name="_TOP"><b><h1><a
href=http://www.csie.ntu.edu.tw/~cjlin/libsvm>LIBSVM</a>  FAQ </h1></b></a>
<b>last modified : </b>
Sun,  1 Apr 2007 00:31:43 GMT
<class="categories">
<li><a
href="#_TOP">All Questions</a>(57)</li>
<ul><b>
<li><a
href="#/Q1:_Some_courses_which_have_used_libsvm_as_a_tool">Q1:_Some_courses_which_have_used_libsvm_as_a_tool</a>(1)</li>
<li><a
href="#/Q2:_Installation_and_running_the_program">Q2:_Installation_and_running_the_program</a>(8)</li>
<li><a
href="#/Q3:_Data_preparation">Q3:_Data_preparation</a>(3)</li>
<li><a
href="#/Q4:_Training_and_prediction">Q4:_Training_and_prediction</a>(28)</li>
<li><a
href="#/Q5:_Probability_outputs">Q5:_Probability_outputs</a>(3)</li>
<li><a
href="#/Q6:_Graphic_interface">Q6:_Graphic_interface</a>(3)</li>
<li><a
href="#/Q7:_Java_version_of_libsvm">Q7:_Java_version_of_libsvm</a>(4)</li>
<li><a
href="#/Q8:_Python_interface">Q8:_Python_interface</a>(5)</li>
<li><a
href="#/Q9:_MATLAB_interface">Q9:_MATLAB_interface</a>(2)</li>
</b></ul>
</li>

<ul><ul class="headlines">
<li class="headlines_item"><a href="#faq1">Some courses which have used libsvm as a tool</a></li>
<li class="headlines_item"><a href="#f201">Where can I find documents of libsvm ?</a></li>
<li class="headlines_item"><a href="#f202">What are changes in previous versions?</a></li>
<li class="headlines_item"><a href="#f203">I would like to cite libsvm. Which paper should I cite ?   </a></li>
<li class="headlines_item"><a href="#f204">I would like to use libsvm in my software. Is there any license problem?</a></li>
<li class="headlines_item"><a href="#f205">Is there a repository of additional tools based on libsvm?</a></li>
<li class="headlines_item"><a href="#f206">On unix machines, I got "error in loading shared libraries" or "cannot open shared object file." What happened ? </a></li>
<li class="headlines_item"><a href="#f207">I have modified the source and would like to build the graphic interface "svm-toy" on MS windows. How should I do it ?</a></li>
<li class="headlines_item"><a href="#f208">I am an MS windows user but why only one (SVM_toy) of those precompiled .exe actually runs ?  </a></li>
<li class="headlines_item"><a href="#f301">Why sometimes not all attributes of a data appear in the training/model files ?</a></li>
<li class="headlines_item"><a href="#f302">What if my data are non-numerical ?</a></li>
<li class="headlines_item"><a href="#f303">Why do you consider sparse format ? Will the training of dense data be much slower ?</a></li>
<li class="headlines_item"><a href="#f401">The output of training C-SVM is like the following. What do they mean?</a></li>
<li class="headlines_item"><a href="#f402">Can you explain more about the model file?</a></li>
<li class="headlines_item"><a href="#f403">Should I use float or double to store numbers in the cache ?</a></li>
<li class="headlines_item"><a href="#f404">How do I choose the kernel?</a></li>
<li class="headlines_item"><a href="#f405">Does libsvm have special treatments for linear SVM?</a></li>
<li class="headlines_item"><a href="#f406">The number of free support vectors is large. What should I do?</a></li>
<li class="headlines_item"><a href="#f407">Should I scale training and testing data in a similar way?</a></li>
<li class="headlines_item"><a href="#f408">Does it make a big difference  if I scale each attribute to [0,1] instead of [-1,1]?</a></li>
<li class="headlines_item"><a href="#f409">The prediction rate is low. How could I improve it?</a></li>
<li class="headlines_item"><a href="#f410">My data are unbalanced. Could libsvm handle such problems?</a></li>
<li class="headlines_item"><a href="#f411">What is the difference between nu-SVC and C-SVC?</a></li>
<li class="headlines_item"><a href="#f412">The program keeps running (without showing any output). What should I do?</a></li>
<li class="headlines_item"><a href="#f413">The program keeps running (with output, i.e. many dots). What should I do?</a></li>
<li class="headlines_item"><a href="#f414">The training time is too long. What should I do?</a></li>
<li class="headlines_item"><a href="#f415">How do I get the decision value(s)?</a></li>
<li class="headlines_item"><a href="#f4151">How do I get the distance between a point and the hyperplane?</a></li>
<li class="headlines_item"><a href="#f416">On 32-bit machines, if I use a large cache (i.e. large -m) on a linux machine, why sometimes I get "segmentation fault ?"</a></li>
<li class="headlines_item"><a href="#f417">How do I disable screen output of svm-train and svm-predict ?</a></li>
<li class="headlines_item"><a href="#f418">I would like to use my own kernel but find out that there are two subroutines for kernel evaluations: k_function() and kernel_function(). Which one should I modify ?</a></li>
<li class="headlines_item"><a href="#f419">What method does libsvm use for multi-class SVM ? Why don't you use the "1-against-the rest" method ?</a></li>
<li class="headlines_item"><a href="#f420">After doing cross validation, why there is no model file outputted ?</a></li>
<li class="headlines_item"><a href="#f421">I would like to try different random partition for cross validation, how could I do it ?</a></li>
<li class="headlines_item"><a href="#f422">I would like to solve L2-SVM (i.e., error term is quadratic). How should I modify the code ?</a></li>
<li class="headlines_item"><a href="#f424">How do I choose parameters for one-class svm as training data are in only one class?</a></li>
<li class="headlines_item"><a href="#f427">Why the code gives NaN (not a number) results?</a></li>
<li class="headlines_item"><a href="#f428">Why on windows sometimes grid.py fails?</a></li>
<li class="headlines_item"><a href="#f429">Why grid.py/easy.py sometimes generates the following warning message?</a></li>
<li class="headlines_item"><a href="#f430">Why the sign of predicted labels and decision values are sometimes reversed?</a></li>
<li class="headlines_item"><a href="#f425">Why training a probability model (i.e., -b 1) takes longer time</a></li>
<li class="headlines_item"><a href="#f426">Why using the -b option does not give me better accuracy?</a></li>
<li class="headlines_item"><a href="#f427">Why using svm-predict -b 0 and -b 1 gives different accuracy values?</a></li>
<li class="headlines_item"><a href="#f501">How can I save images drawn by svm-toy?</a></li>
<li class="headlines_item"><a href="#f502">I press the "load" button to load data points but why svm-toy does not draw them ?</a></li>
<li class="headlines_item"><a href="#f503">I would like svm-toy to handle more than three classes of data, what should I do ?</a></li>
<li class="headlines_item"><a href="#f601">What is the difference between Java version and C++ version of libsvm?</a></li>
<li class="headlines_item"><a href="#f602">Is the Java version significantly slower than the C++ version?</a></li>
<li class="headlines_item"><a href="#f603">While training I get the following error message: java.lang.OutOfMemoryError. What is wrong?</a></li>
<li class="headlines_item"><a href="#f604">Why you have the main source file svm.m4 and then transform it to svm.java?</a></li>
<li class="headlines_item"><a href="#f702">On MS windows, why does python fail to load the pyd file?</a></li>
<li class="headlines_item"><a href="#f703">How to modify the python interface on MS windows and rebuild the .pyd file ?</a></li>
<li class="headlines_item"><a href="#f704">Except the python-C++ interface provided, could I use Jython to call libsvm ?</a></li>
<li class="headlines_item"><a href="#f705">How could I install the python interface on Mac OS? </a></li>
<li class="headlines_item"><a href="#f706">I typed "make" on a unix system, but it says "Python.h: No such file or directory?"</a></li>
<li class="headlines_item"><a href="#f801">I compile the MATLAB interface without problem, but why errors occur while running it?</a></li>
<li class="headlines_item"><a href="#f802">Does the MATLAB interface provide a function to do scaling?</a></li>
</ul></ul>


<hr size="5" noshade />
<p/>
  
<a name="/Q1:_Some_courses_which_have_used_libsvm_as_a_tool"></a>
<a name="faq1"><b>Q: Some courses which have used libsvm as a tool</b></a>
<br/>                                                                                
<ul>
<li><a href=http://lmb.informatik.uni-freiburg.de/lectures/svm_seminar/>Institute for Computer Science,           
Faculty of Applied Science, University of Freiburg, Germany 
</a>
<li> <a href=http://www.cs.vu.nl/~elena/ml.html>
Division of Mathematics and Computer Science. 
Faculteit der Exacte Wetenschappen 
Vrije Universiteit, The Netherlands. </a>
<li>
<a href=http://www.cae.wisc.edu/~ece539/matlab/>
Electrical and Computer Engineering Department, 
University of Wisconsin-Madison 
</a>
<li>
<a href=http://www.hpl.hp.com/personal/Carl_Staelin/cs236601/project.html>
Technion (Israel Institute of Technology), Israel.
<li>
<a href=http://www.cise.ufl.edu/~fu/learn.html>
Computer and Information Sciences Dept., University of Florida</a>
<li>
<a href=http://www.uonbi.ac.ke/acad_depts/ics/course_material/machine_learning/ML_and_DM_Resources.html>
The Institute of Computer Science,
University of Nairobi, Kenya.</a>
<li>
<a href=http://cerium.raunvis.hi.is/~tpr/courseware/svm/hugbunadur.html>
Applied Mathematics and Computer Science, University of Iceland.
<li>
<a href=http://chicago05.mlss.cc/tiki/tiki-read_article.php?articleId=2>
SVM tutorial in machine learning
summer school, University of Chicago, 2005.
</a>
</ul>
<p align="right">
<a href="#_TOP">[Go Top]</a>  
<hr/>
  <a name="/Q2:_Installation_and_running_the_program"></a>
<a name="f201"><b>Q: Where can I find documents of libsvm ?</b></a>
<br/>                                                                                
<p>
In the package there is a README file which 
details all options, data format, and library calls.
The model selection tool and the python interface
have a separate README under the directory python.
The guide
<A HREF="http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf">
A practical guide to support vector classification
</A> shows beginners how to train/test their data.
The paper <a href="http://www.csie.ntu.edu.tw/~cjlin/papers/libsvm.pdf">LIBSVM
: a library for support vector machines</a> discusses the implementation of
libsvm in detail.
<p align="right">
<a href="#_TOP">[Go Top]</a>  
<hr/>
  <a name="/Q2:_Installation_and_running_the_program"></a>
<a name="f202"><b>Q: What are changes in previous versions?</b></a>
<br/>                                                                                
<p>See <a href="http://www.csie.ntu.edu.tw/~cjlin/libsvm/log">the change log</a>.

<p> You can download earlier versions 
<a href="http://www.csie.ntu.edu.tw/~cjlin/libsvm/oldfiles">here</a>.
<p align="right">
<a href="#_TOP">[Go Top]</a>  
<hr/>
  <a name="/Q2:_Installation_and_running_the_program"></a>
<a name="f203"><b>Q: I would like to cite libsvm. Which paper should I cite ?   </b></a>
<br/>                                                                                
<p>
Please cite the following document:
<p>
Chih-Chung Chang and Chih-Jen Lin, LIBSVM
: a library for support vector machines, 2001.
Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm
<p>
The bibtex format is as follows
<pre>
@Manual{CC01a,
  author =	 {Chih-Chung Chang and Chih-Jen Lin},
  title =	 {{LIBSVM}: a library for support vector machines},
  year =	 {2001},
  note =	 {Software available at \url{http://www.csie.ntu.edu.tw/~cjlin/libsvm}}
}
</pre>
<p align="right">
<a href="#_TOP">[Go Top]</a>  
<hr/>
  <a name="/Q2:_Installation_and_running_the_program"></a>
<a name="f204"><b>Q: I would like to use libsvm in my software. Is there any license problem?</b></a>
<br/>                                                                                
<p>
The libsvm license ("the modified BSD license")
is compatible with many
free software licenses such as GPL. Hence, it is very easy to
use libsvm in your software.
It can also be used in commercial products.
<p align="right">
<a href="#_TOP">[Go Top]</a>  
<hr/>
  <a name="/Q2:_Installation_and_running_the_program"></a>
<a name="f205"><b>Q: Is there a repository of additional tools based on libsvm?</b></a>
<br/>                                                                                
<p>
Yes, see <a href="http://www.csie.ntu.edu.tw/~cjlin/libsvmtools">libsvm 
tools</a>
<p align="right">
<a href="#_TOP">[Go Top]</a>  
<hr/>
  <a name="/Q2:_Installation_and_running_the_program"></a>
<a name="f206"><b>Q: On unix machines, I got "error in loading shared libraries" or "cannot open shared object file." What happened ? </b></a>
<br/>                                                                                

<p>
This usually happens if you compile the code
on one machine and run it on another which has incompatible
libraries.
Try to recompile the program on that machine or use static linking.
<p align="right">
<a href="#_TOP">[Go Top]</a>  
<hr/>
  <a name="/Q2:_Installation_and_running_the_program"></a>
<a name="f207"><b>Q: I have modified the source and would like to build the graphic interface "svm-toy" on MS windows. How should I do it ?</b></a>
<br/>                                                                                

<p>
Build it as a project by choosing "Win32 Project."
On the other hand, for "svm-train" and "svm-predict"
you want to choose "Win32 Console Project."
After libsvm 2.5, you can also use the file Makefile.win.
See details in README.


<p>
If you are not using Makefile.win and see the following 
link error
<pre>
LIBCMTD.lib(wwincrt0.obj) : error LNK2001: unresolved external symbol
_wWinMain@16
</pre>
you may have selected a wrong project type.
<p align="right">
<a href="#_TOP">[Go Top]</a>  
<hr/>
  <a name="/Q2:_Installation_and_running_the_program"></a>
<a name="f208"><b>Q: I am an MS windows user but why only one (SVM_toy) of those precompiled .exe actually runs ?  </b></a>
<br/>                                                                                

<p>
You need to open a command window 
and type  svmtrain.exe to see all options.
Some examples are in README file.
<p align="right">
<a href="#_TOP">[Go Top]</a>  
<hr/>
  <a name="/Q3:_Data_preparation"></a>
<a name="f301"><b>Q: Why sometimes not all attributes of a data appear in the training/model files ?</b></a>
<br/>                                                                                
<p>
libsvm uses the so called "sparse" format where zero
values do not need to be stored. Hence a data with attributes
<pre>
1 0 2 0
</pre>
is represented as
<pre>
1:1 3:2
</pre>
<p align="right">
<a href="#_TOP">[Go Top]</a>  
<hr/>
  <a name="/Q3:_Data_preparation"></a>
<a name="f302"><b>Q: What if my data are non-numerical ?</b></a>
<br/>                                                                                
<p>
Currently libsvm supports only numerical data.
You may have to change non-numerical data to 
numerical. For example, you can use several
binary attributes to represent a categorical
attribute.
<p align="right">
<a href="#_TOP">[Go Top]</a>  
<hr/>
  <a name="/Q3:_Data_preparation"></a>
<a name="f303"><b>Q: Why do you consider sparse format ? Will the training of dense data be much slower ?</b></a>
<br/>                                                                                
<p>
This is a controversial issue. The kernel
evaluation (i.e. inner product) of sparse vectors is slower 
so the total training time can be at least twice or three times
of that using the dense format.
However, we cannot support only dense format as then we CANNOT
handle extremely sparse cases. Simplicity of the code is another
concern. Right now we decide to support
the sparse format only.
<p align="right">
<a href="#_TOP">[Go Top]</a>  
<hr/>
  <a name="/Q4:_Training_and_prediction"></a>
<a name="f401"><b>Q: The output of training C-SVM is like the following. What do they mean?</b></a>
<br/>                                                                                
<br>optimization finished, #iter = 219
<br>nu = 0.431030
<br>obj = -100.877286, rho = 0.424632
<br>nSV = 132, nBSV = 107
<br>Total nSV = 132
<p>
obj is the optimal objective value of the dual SVM problem.
rho is the bias term in the decision function
sgn(w^Tx - rho).
nSV and nBSV are number of support vectors and bounded support
vectors (i.e., alpha_i = C). nu-svm is a somewhat equivalent
form of C-SVM where C is replaced by nu. nu simply shows the

?? 快捷鍵說明

復制代碼 Ctrl + C
搜索代碼 Ctrl + F
全屏模式 F11
切換主題 Ctrl + Shift + D
顯示快捷鍵 ?
增大字號 Ctrl + =
減小字號 Ctrl + -
亚洲欧美第一页_禁久久精品乱码_粉嫩av一区二区三区免费野_久草精品视频
成人激情小说乱人伦| 午夜精品一区二区三区三上悠亚| 日韩一级高清毛片| 色婷婷一区二区三区四区| 粉嫩13p一区二区三区| 国产成人午夜99999| 国产一区二区调教| 大白屁股一区二区视频| 成人avav在线| 在线观看不卡一区| 九九精品视频在线看| 成人午夜伦理影院| 成人激情黄色小说| 在线视频一区二区免费| 欧美日产在线观看| 欧美成人一级视频| 国产欧美一区在线| 综合激情网...| 亚洲国产成人va在线观看天堂| 亚洲欧美日韩国产手机在线| 综合网在线视频| 午夜精品久久久久久| 久久av资源站| 成人免费视频一区二区| 欧洲av一区二区嗯嗯嗯啊| 67194成人在线观看| 国产亚洲精品超碰| 国产精品国产a| 一卡二卡欧美日韩| 精品一区二区三区在线播放| 国产成人av一区二区三区在线观看| 91伊人久久大香线蕉| 91麻豆精品国产91久久久使用方法 | 日韩和的一区二区| 国产在线视频一区二区三区| 国产精品123| 在线区一区二视频| 国产亚洲美州欧州综合国| 亚洲精品国久久99热| 蜜桃久久久久久久| 韩国v欧美v亚洲v日本v| 欧美丝袜丝nylons| 精品国产伦理网| 亚洲色图欧洲色图婷婷| 91精品国产福利| 久久精品一区蜜桃臀影院| 亚洲福利电影网| 懂色av一区二区三区免费观看| 日本韩国欧美三级| 亚洲国产精品激情在线观看| 美女视频黄久久| 91久久精品国产91性色tv | 欧美久久久久久久久| 国产精品福利一区二区| 国内精品在线播放| 欧美日韩国产123区| 日韩一区在线播放| 国产黄人亚洲片| 日韩女优av电影| 天堂蜜桃91精品| 欧美日韩在线不卡| 亚洲黄网站在线观看| 不卡一区中文字幕| 日本一区二区三级电影在线观看 | 韩国一区二区在线观看| 欧美一级理论片| 亚洲小少妇裸体bbw| 91高清视频在线| 亚洲免费av高清| 在线亚洲+欧美+日本专区| 国产欧美精品一区二区色综合朱莉| 久久99精品一区二区三区 | 国产精品99久久久| 日韩免费性生活视频播放| 天天射综合影视| 欧美精品免费视频| 日韩高清不卡一区二区三区| 欧美区一区二区三区| 午夜亚洲福利老司机| 亚洲色欲色欲www在线观看| 韩国理伦片一区二区三区在线播放| 91精品蜜臀在线一区尤物| 琪琪一区二区三区| 欧美一二三在线| 韩国在线一区二区| 国产精品久久久久久久浪潮网站 | 中文在线一区二区 | 蜜桃av一区二区在线观看| 欧美一级高清大全免费观看| 精品亚洲国内自在自线福利| 国产日韩影视精品| 色悠悠久久综合| 午夜成人在线视频| 欧美大片拔萝卜| 福利电影一区二区| 一区二区三区小说| 日韩三级伦理片妻子的秘密按摩| 国内精品在线播放| 亚洲视频中文字幕| 欧美精品在线观看一区二区| 国产自产2019最新不卡| 亚洲欧洲国产日韩| 制服丝袜中文字幕亚洲| 国产乱色国产精品免费视频| 亚洲男人的天堂在线aⅴ视频| 欧美久久久久免费| 国产**成人网毛片九色| 亚洲国产精品久久久久秋霞影院| 日韩欧美在线综合网| jlzzjlzz亚洲日本少妇| 日韩制服丝袜先锋影音| 国产日韩欧美a| 欧美精品亚洲一区二区在线播放| 国产成人av一区二区三区在线| 亚洲免费观看高清| 久久综合久色欧美综合狠狠| 在线日韩av片| 国产不卡视频在线观看| 婷婷六月综合网| 亚洲品质自拍视频| 久久精品网站免费观看| 欧美日韩精品电影| 成人h动漫精品| 久草在线在线精品观看| 午夜电影网亚洲视频| 亚洲欧美日韩人成在线播放| 欧美精品一区二区三区蜜桃视频| 日本韩国视频一区二区| 成人av综合在线| 国产揄拍国内精品对白| 三级在线观看一区二区 | 免费高清在线一区| 一区二区三区蜜桃网| 国产精品成人在线观看| 久久嫩草精品久久久精品一| 日韩一区二区麻豆国产| 欧美亚洲国产bt| 91黄色免费网站| 色综合网色综合| 一本色道久久综合亚洲精品按摩| 国产美女在线精品| 国内成人精品2018免费看| 日韩不卡一二三区| 视频一区在线播放| 日韩成人av影视| 午夜精品久久久久久久久久久 | 亚洲天堂av老司机| 一区二区三区四区中文字幕| 日韩欧美电影一二三| 欧美精品乱码久久久久久| 欧美日韩中文字幕一区二区| 91麻豆国产福利在线观看| av一二三不卡影片| 91在线观看地址| 91在线国产福利| 色综合天天视频在线观看| 色香蕉成人二区免费| 91女厕偷拍女厕偷拍高清| 99久久婷婷国产综合精品| 91污片在线观看| 欧美在线free| 91精品国产一区二区三区蜜臀| 日韩免费观看高清完整版| 日韩精品中文字幕一区二区三区| 日韩欧美亚洲国产精品字幕久久久| 91精品国产高清一区二区三区| 欧美电影免费观看高清完整版在| 精品电影一区二区| 奇米888四色在线精品| 国产成人av资源| 91美女片黄在线| 欧美日韩国产欧美日美国产精品| 欧美久久高跟鞋激| 精品国产一区二区三区av性色| 久久精品亚洲精品国产欧美| 中文字幕一区二区三区乱码在线| 一区二区三区色| 精品一区二区三区免费观看 | 欧美日韩久久一区| 日韩欧美三级在线| 国产精品午夜免费| 亚洲一区二区欧美激情| 黄网站免费久久| 日本韩国一区二区| 欧美电影免费提供在线观看| 中文字幕亚洲一区二区va在线| 日韩中文字幕亚洲一区二区va在线| 蜜臀精品久久久久久蜜臀| 在线观看日韩高清av| 日韩免费高清电影| 亚洲视频一区在线| 欧美a一区二区| 一本到不卡精品视频在线观看| 欧美大片免费久久精品三p| 亚洲色图欧洲色图婷婷| 国产在线精品国自产拍免费| 91久久国产最好的精华液| 久久久午夜电影| 婷婷中文字幕一区三区| 成人国产精品免费网站|