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<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>
Mon, 29 Mar 2010 12:08:25 GMT
<class="categories">
<li><a
href="#_TOP">All Questions</a>(76)</li>
<ul><b>
<li><a
href="#/Q1:_Some_sample_uses_of_libsvm">Q1:_Some_sample_uses_of_libsvm</a>(2)</li>
<li><a
href="#/Q2:_Installation_and_running_the_program">Q2:_Installation_and_running_the_program</a>(10)</li>
<li><a
href="#/Q3:_Data_preparation">Q3:_Data_preparation</a>(7)</li>
<li><a
href="#/Q4:_Training_and_prediction">Q4:_Training_and_prediction</a>(33)</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>(9)</li>
</b></ul>
</li>
<ul><ul class="headlines">
<li class="headlines_item"><a href="#faq101">Some courses which have used libsvm as a tool</a></li>
<li class="headlines_item"><a href="#faq102">Some applications which have used libsvm </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">Where are change log and earlier 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="#f209">What is the difference between "." and "*" outputed during training? </a></li>
<li class="headlines_item"><a href="#f210">Why occasionally the program (including MATLAB or other interfaces) crashes and gives a segmentation fault?</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="#f304">Why sometimes the last line of my data is not read by svm-train?</a></li>
<li class="headlines_item"><a href="#f305">Is there a program to check if my data are in the correct format?</a></li>
<li class="headlines_item"><a href="#f306">May I put comments in data files?</a></li>
<li class="headlines_item"><a href="#f307">How to convert other data formats to LIBSVM format?</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="#f4141">Does shrinking always help?</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="#f4201">Why my cross-validation results are different from those in the Practical Guide?</a></li>
<li class="headlines_item"><a href="#f421">But on some systems CV accuracy is the same in several runs. How could I use different data partitions?</a></li>
<li class="headlines_item"><a href="#f422">I would like to solve L2-loss 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="#f431">I don't know class labels of test data. What should I put in the first column of the test file?</a></li>
<li class="headlines_item"><a href="#f432">How can I use OpenMP to parallelize LIBSVM on a multicore/shared-memory computer?</a></li>
<li class="headlines_item"><a href="#f433">How could I know which training instances are support vectors?</a></li>
<li class="headlines_item"><a href="#f425">Why training a probability model (i.e., -b 1) takes a 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="#f8011">On 64bit Windows 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>
<li class="headlines_item"><a href="#f803">How could I use MATLAB interface for parameter selection?</a></li>
<li class="headlines_item"><a href="#f8031">I use MATLAB parallel programming toolbox on a multi-core environment for parameter selection. Why the program is even slower?</a></li>
<li class="headlines_item"><a href="#f8032">How do I use LIBSVM with OpenMP under MATLAB?</a></li>
<li class="headlines_item"><a href="#f804">How could I generate the primal variable w of linear SVM?</a></li>
<li class="headlines_item"><a href="#f805">Is there an OCTAVE interface for libsvm?</a></li>
<li class="headlines_item"><a href="#f806">How to handle the name conflict between svmtrain in the libsvm matlab interface and that in MATLAB bioinformatics toolbox?</a></li>
</ul></ul>
<hr size="5" noshade />
<p/>
<a name="/Q1:_Some_sample_uses_of_libsvm"></a>
<a name="faq101"><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="/Q1:_Some_sample_uses_of_libsvm"></a>
<a name="faq102"><b>Q: Some applications which have used libsvm </b></a>
<br/>
<ul>
<li>
<a href=http://people.csail.mit.edu/jjl/libpmk/>LIBPMK: A Pyramid Match Toolkit</a>
</li>
<li><a href=http://maltparser.org/>Maltparser</a>:
a system for data-driven dependency parsing
</li>
<li>
<a href=http://www.pymvpa.org/>PyMVPA: python tool for classifying neuroimages</a>
</li>
<li>
<a href=http://solpro.proteomics.ics.uci.edu/>
SOLpro: protein solubility predictor
</a>
</li>
<li>
<a href=http://icb.med.cornell.edu/wiki/index.php/BDVAL>
BDVAL</a>: biomarker discovery in high-throughput datasets.
</li>
<li><a href=http://johel.m.free.fr/demo_045.htm>
Realtime object recognition</a>
</li>
</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: Where are change log and earlier 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
<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">
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<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">
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<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">
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<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">
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<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.
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<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">
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<hr/>
<a name="/Q2:_Installation_and_running_the_program"></a>
<a name="f209"><b>Q: What is the difference between "." and "*" outputed during training? </b></a>
<br/>
<p>
"." means every 1,000 iterations (or every #data
iterations is your #data is less than 1,000).
"*" means that after iterations of using
a smaller shrunk problem,
we reset to use the whole set. See the
<a href=../papers/libsvm.pdf>implementation document</a> for details.
<p align="right">
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<hr/>
<a name="/Q2:_Installation_and_running_the_program"></a>
<a name="f210"><b>Q: Why occasionally the program (including MATLAB or other interfaces) crashes and gives a segmentation fault?</b></a>
<br/>
<p>
Very likely the program consumes too much memory than what the
operating system can provide. Try a smaller data and see if the
program still crashes.
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<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>
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<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">
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<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.
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<hr/>
<a name="/Q3:_Data_preparation"></a>
<a name="f304"><b>Q: Why sometimes the last line of my data is not read by svm-train?</b></a>
<br/>
<p>
We assume that you have '\n' in the end of
each line. So please press enter in the end
of your last line.
<p align="right">
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<hr/>
<a name="/Q3:_Data_preparation"></a>
<a name="f305"><b>Q: Is there a program to check if my data are in the correct format?</b></a>
<br/>
<p>
The svm-train program in libsvm conducts only a simple check of the input data. To do a
detailed check, after libsvm 2.85, you can use the python script tools/checkdata.py. See tools/README for details.
<p align="right">
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<hr/>
<a name="/Q3:_Data_preparation"></a>
<a name="f306"><b>Q: May I put comments in data files?</b></a>
<br/>
<p>
No, for simplicity we don't support that.
However, you can easily preprocess your data before
using libsvm. For example,
if you have the following data
<pre>
test.txt
1 1:2 2:1 # proten A
</pre>
then on unix machines you can do
<pre>
cut -d '#' -f 1 < test.txt > test.features
cut -d '#' -f 2 < test.txt > test.comments
svm-predict test.feature train.model test.predicts
paste -d '#' test.predicts test.comments | sed 's/#/ #/' > test.results
</pre>
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<hr/>
<a name="/Q3:_Data_preparation"></a>
<a name="f307"><b>Q: How to convert other data formats to LIBSVM format?</b></a>
<br/>
<p>
It depends on your data format.
We have a simple C code to transfer space/colon
separated format to libsvm format. Please contact
us if needed.
<p>
Alternatively, a simple way is to use
libsvmwrite in the libsvm matlab/octave interface.
Take a CSV (colon separated format) file
in UCI machine learning repository as an example.
We download <a href=http://archive.ics.uci.edu/ml/machine-learning-databases/spect/SPECTF.train>SPECTF.train</a>.
Labels are in the first column. The following steps produce
a file in the libsvm format.
<pre>
matlab> SPECTF = csvread('SPECTF.train'); % read a csv file
matlab> labels = SPECTF(:, 1); % labels from the 1st column
matlab> features = SPECTF(:, 2:end);
matlab> features_sparse = sparse(features); % features must be in a sparse matrix
matlab> libsvmwrite('SPECTFlibsvm.train', labels, features_sparse);
</pre>
The tranformed data are stored in SPECTFlibsvm.train.
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<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
corresponding parameter. More details are in
<a href="http://www.csie.ntu.edu.tw/~cjlin/papers/libsvm.pdf">
libsvm document</a>.
<p align="right">
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<hr/>
<a name="/Q4:_Training_and_prediction"></a>
<a name="f402"><b>Q: Can you explain more about the model file?</b></a>
<br/>
<p>
After the parameters, each line represents a support vector.
Support vectors are listed in the order of "labels" listed earlier.
(i.e., those from the first class in the "labels" list are
grouped first, and so on.)
If k is the total number of classes,
in front of a support vector in class j, there are
k-1 coefficients
y*alpha where alpha are dual solution of the
following two class problems:
<br>
1 vs j, 2 vs j, ..., j-1 vs j, j vs j+1, j vs j+2, ..., j vs k
<br>
and y=1 in first j-1 coefficients, y=-1 in the remaining
k-j coefficients.
For example, if there are 4 classes, the file looks like:
<pre>
+-+-+-+--------------------+
|1|1|1| |
|v|v|v| SVs from class 1 |
|2|3|4| |
+-+-+-+--------------------+
|1|2|2| |
|v|v|v| SVs from class 2 |
|2|3|4| |
+-+-+-+--------------------+
|1|2|3| |
|v|v|v| SVs from class 3 |
|3|3|4| |
+-+-+-+--------------------+
|1|2|3| |
|v|v|v| SVs from class 4 |
|4|4|4| |
+-+-+-+--------------------+
</pre>
See also
<a href="#f804"> an illustration using
MATLAB/OCTAVE.</a>
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<hr/>
<a name="/Q4:_Training_and_prediction"></a>
<a name="f403"><b>Q: Should I use float or double to store numbers in the cache ?</b></a>
<br/>
<p>
We have float as the default as you can store more numbers
in the cache.
In general this is good enough but for few difficult
cases (e.g. C very very large) where solutions are huge
numbers, it might be possible that the numerical precision is not
enough using only float.
<p align="right">
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<hr/>
<a name="/Q4:_Training_and_prediction"></a>
<a name="f404"><b>Q: How do I choose the kernel?</b></a>
<br/>
<p>
In general we suggest you to try the RBF kernel first.
A recent result by Keerthi and Lin
(<a href=http://www.csie.ntu.edu.tw/~cjlin/papers/limit.pdf>
download paper here</a>)
shows that if RBF is used with model selection,
then there is no need to consider the linear kernel.
The kernel matrix using sigmoid may not be positive definite
and in general it's accuracy is not better than RBF.
(see the paper by Lin and Lin
(<a href=http://www.csie.ntu.edu.tw/~cjlin/papers/tanh.pdf>
download paper here</a>).
Polynomial kernels are ok but if a high degree is used,
numerical difficulties tend to happen
(thinking about dth power of (<1) goes to 0
and (>1) goes to infinity).
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<hr/>
<a name="/Q4:_Training_and_prediction"></a>
<a name="f405"><b>Q: Does libsvm have special treatments for linear SVM?</b></a>
<br/>
<p>
No, libsvm solves linear/nonlinear SVMs by the
same way.
Some tricks may save training/testing time if the
linear kernel is used,
so libsvm is <b>NOT</b> particularly efficient for linear SVM,
especially when
C is large and
the number of data is much larger
than the number of attributes.
You can either
<ul>
<li>
Use small C only. We have shown in the following paper
that after C is larger than a certain threshold,
the decision function is the same.
<p>
<a href="http://guppy.mpe.nus.edu.sg/~mpessk/">S. S. Keerthi</a>
and
<B>C.-J. Lin</B>.
<A HREF="papers/limit.pdf">
Asymptotic behaviors of support vector machines with
Gaussian kernel
</A>
.
<I><A HREF="http://mitpress.mit.edu/journal-home.tcl?issn=08997667">Neural Computation</A></I>, 15(2003), 1667-1689.
<li>
Check <a href=http://www.csie.ntu.edu.tw/~cjlin/liblinear>liblinear</a>,
which is designed for large-scale linear classification.
</ul>
<p> Please also see our <a href=../papers/guide/guide.pdf>SVM guide</a>
on the discussion of using RBF and linear
kernels.
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<hr/>
<a name="/Q4:_Training_and_prediction"></a>
<a name="f406"><b>Q: The number of free support vectors is large. What should I do?</b></a>
<br/>
<p>
This usually happens when the data are overfitted.
If attributes of your data are in large ranges,
try to scale them. Then the region
of appropriate parameters may be larger.
Note that there is a scale program
in libsvm.
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<hr/>
<a name="/Q4:_Training_and_prediction"></a>
<a name="f407"><b>Q: Should I scale training and testing data in a similar way?</b></a>
<br/>
<p>
Yes, you can do the following:
<pre>
> svm-scale -s scaling_parameters train_data > scaled_train_data
> svm-scale -r scaling_parameters test_data > scaled_test_data
</pre>
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<hr/>
<a name="/Q4:_Training_and_prediction"></a>
<a name="f408"><b>Q: Does it make a big difference if I scale each attribute to [0,1] instead of [-1,1]?</b></a>
<br/>
<p>
For the linear scaling method, if the RBF kernel is
used and parameter selection is conducted, there
is no difference. Assume Mi and mi are
respectively the maximal and minimal values of the
ith attribute. Scaling to [0,1] means
<pre>
x'=(x-mi)/(Mi-mi)
</pre>
For [-1,1],
<pre>
x''=2(x-mi)/(Mi-mi)-1.
</pre>
In the RBF kernel,
<pre>
x'-y'=(x-y)/(Mi-mi), x''-y''=2(x-y)/(Mi-mi).
</pre>
Hence, using (C,g) on the [0,1]-scaled data is the
same as (C,g/2) on the [-1,1]-scaled data.
<p> Though the performance is the same, the computational
time may be different. For data with many zero entries,
[0,1]-scaling keeps the sparsity of input data and hence
may save the time.
<p align="right">
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<hr/>
<a name="/Q4:_Training_and_prediction"></a>
<a name="f409"><b>Q: The prediction rate is low. How could I improve it?</b></a>
<br/>
<p>
Try to use the model selection tool grid.py in the python
directory find
out good parameters. To see the importance of model selection,
please
see my talk:
<A HREF="http://www.csie.ntu.edu.tw/~cjlin/talks/freiburg.pdf">
A practical guide to support vector
classification
</A>
<p align="right">
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<hr/>
<a name="/Q4:_Training_and_prediction"></a>
<a name="f410"><b>Q: My data are unbalanced. Could libsvm handle such problems?</b></a>
<br/>
<p>
Yes, there is a -wi options. For example, if you use
<pre>
> svm-train -s 0 -c 10 -w1 1 -w-1 5 data_file
</pre>
<p>
the penalty for class "-1" is larger.
Note that this -w option is for C-SVC only.
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<hr/>
<a name="/Q4:_Training_and_prediction"></a>
<a name="f411"><b>Q: What is the difference between nu-SVC and C-SVC?</b></a>
<br/>
<p>
Basically they are the same thing but with different
parameters. The range of C is from zero to infinity
but nu is always between [0,1]. A nice property
of nu is that it is related to the ratio of
support vectors and the ratio of the training
error.
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<hr/>
<a name="/Q4:_Training_and_prediction"></a>
<a name="f412"><b>Q: The program keeps running (without showing any output). What should I do?</b></a>
<br/>
<p>
You may want to check your data. Each training/testing
data must be in one line. It cannot be separated.
In addition, you have to remove empty lines.
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<hr/>
<a name="/Q4:_Training_and_prediction"></a>
<a name="f413"><b>Q: The program keeps running (with output, i.e. many dots). What should I do?</b></a>
<br/>
<p>
In theory libsvm guarantees to converge.
Therefore, this means you are
handling ill-conditioned situations
(e.g. too large/small parameters) so numerical
difficulties occur.
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<hr/>
<a name="/Q4:_Training_and_prediction"></a>
<a name="f414"><b>Q: The training time is too long. What should I do?</b></a>
<br/>
<p>
For large problems, please specify enough cache size (i.e.,
-m).
Slow convergence may happen for some difficult cases (e.g. -c is large).
You can try to use a looser stopping tolerance with -e.
If that still doesn't work, you may train only a subset of the data.
You can use the program subset.py in the directory "tools"
to obtain a random subset.
<p>
If you have extremely large data and face this difficulty, please
contact us. We will be happy to discuss possible solutions.
<p> When using large -e, you may want to check if -h 0 (no shrinking) or -h 1 (shrinking) is faster.
See a related question below.
<p align="right">
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<hr/>
<a name="/Q4:_Training_and_prediction"></a>
<a name="f4141"><b>Q: Does shrinking always help?</b></a>
<br/>
<p>
If the number of iterations is high, then shrinking
often helps.
However, if the number of iterations is small
(e.g., you specify a large -e), then
probably using -h 0 (no shrinking) is better.
See the
<a href=../papers/libsvm.pdf>implementation document</a> for details.
<p align="right">
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<hr/>
<a name="/Q4:_Training_and_prediction"></a>
<a name="f415"><b>Q: How do I get the decision value(s)?</b></a>
<br/>
<p>
We print out decision values for regression. For classification,
we solve several binary SVMs for multi-class cases. You
can obtain values by easily calling the subroutine
svm_predict_values. Their corresponding labels
can be obtained from svm_get_labels.
Details are in
README of libsvm package.
<p>
We do not recommend the following. But if you would
like to get values for
TWO-class classification with labels +1 and -1
(note: +1 and -1 but not things like 5 and 10)
in the easiest way, simply add
<pre>
printf("%f\n", dec_values[0]*model->label[0]);
</pre>
after the line
<pre>
svm_predict_values(model, x, dec_values);
</pre>
of the file svm.cpp.
Positive (negative)
decision values correspond to data predicted as +1 (-1).
<p align="right">
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<hr/>
<a name="/Q4:_Training_and_prediction"></a>
<a name="f4151"><b>Q: How do I get the distance between a point and the hyperplane?</b></a>
<br/>
<p>
The distance is |decision_value| / |w|.
We have |w|^2 = w^Tw = alpha^T Q alpha = 2*(dual_obj + sum alpha_i).
Thus in svm.cpp please find the place
where we calculate the dual objective value
(i.e., the subroutine Solve())
and add a statement to print w^Tw.
<p align="right">
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<hr/>
<a name="/Q4:_Training_and_prediction"></a>
<a name="f416"><b>Q: On 32-bit machines, if I use a large cache (i.e. large -m) on a linux machine, why sometimes I get "segmentation fault ?"</b></a>
<br/>
<p>
On 32-bit machines, the maximum addressable
memory is 4GB. The Linux kernel uses 3:1
split which means user space is 3G and
kernel space is 1G. Although there are
3G user space, the maximum dynamic allocation
memory is 2G. So, if you specify -m near 2G,
the memory will be exhausted. And svm-train
will fail when it asks more memory.
For more details, please read
<a href=http://groups.google.com/groups?hl=en&lr=&ie=UTF-8&selm=3BA164F6.BAFA4FB%40daimi.au.dk>
this article</a>.
<p>
The easiest solution is to switch to a
64-bit machine.
Otherwise, there are two ways to solve this. If your
machine supports Intel's PAE (Physical Address
Extension), you can turn on the option HIGHMEM64G
in Linux kernel which uses 4G:4G split for
kernel and user space. If you don't, you can
try a software `tub' which can eliminate the 2G
boundary for dynamic allocated memory. The `tub'
is available at
<a href=http://www.bitwagon.com/tub.html>http://www.bitwagon.com/tub.html</a>.
<!--
This may happen only when the cache is large, but each cached row is
not large enough. <b>Note:</b> This problem is specific to
gnu C library which is used in linux.
The solution is as follows:
<p>
In our program we have malloc() which uses two methods
to allocate memory from kernel. One is
sbrk() and another is mmap(). sbrk is faster, but mmap
has a larger address
space. So malloc uses mmap only if the wanted memory size is larger
than some threshold (default 128k).
In the case where each row is not large enough (#elements < 128k/sizeof(float)) but we need a large cache ,
the address space for sbrk can be exhausted. The solution is to
lower the threshold to force malloc to use mmap
and increase the maximum number of chunks to allocate
with mmap.
<p>
Therefore, in the main program (i.e. svm-train.c) you want
to have
<pre>
#include <malloc.h>
</pre>
and then in main():
<pre>
mallopt(M_MMAP_THRESHOLD, 32768);
mallopt(M_MMAP_MAX,1000000);
</pre>
You can also set the environment variables instead
of writing them in the program:
<pre>
$ M_MMAP_MAX=1000000 M_MMAP_THRESHOLD=32768 ./svm-train .....
</pre>
More information can be found by
<pre>
$ info libc "Malloc Tunable Parameters"
</pre>
-->
<p align="right">
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<hr/>
<a name="/Q4:_Training_and_prediction"></a>
<a name="f417"><b>Q: How do I disable screen output of svm-train and svm-predict ?</b></a>
<br/>
<p>
For commend-line users, use the -q option:
<pre>
> ./svm-train -q heart_scale
</pre>
<p>
For library users, set the global variable
<pre>
extern void (*svm_print_string) (const char *);
</pre>
to specify the output format. You can disable the output by the following steps:
<ol>
<li>
Declare a function to output nothing:
<pre>
void print_null(const char *s) {}
</pre>
</li>
<li>
Assign the output function of libsvm by
<pre>
svm_print_string = &print_null;
</pre>
</li>
</ol>
Finally, a way used in earlier libsvm
is by updating svm.cpp from
<pre>
#if 1
void info(const char *fmt,...)
</pre>
to
<pre>
#if 0
void info(const char *fmt,...)
</pre>
<p align="right">
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<hr/>
<a name="/Q4:_Training_and_prediction"></a>
<a name="f418"><b>Q: 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 ?</b></a>
<br/>
<p>
The reason why we have two functions is as follows:
For the RBF kernel exp(-g |xi - xj|^2), if we calculate
xi - xj first and then the norm square, there are 3n operations.
Thus we consider exp(-g (|xi|^2 - 2dot(xi,xj) +|xj|^2))
and by calculating all |xi|^2 in the beginning,
the number of operations is reduced to 2n.
This is for the training. For prediction we cannot
do this so a regular subroutine using that 3n operations is
needed.
The easiest way to have your own kernel is
to put the same code in these two
subroutines by replacing any kernel.
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<a name="f419"><b>Q: What method does libsvm use for multi-class SVM ? Why don't you use the "1-against-the rest" method ?</b></a>
<br/>
<p>
It is one-against-one. We chose it after doing the following
comparison:
C.-W. Hsu and C.-J. Lin.
<A HREF="http://www.csie.ntu.edu.tw/~cjlin/papers/multisvm.pdf">
A comparison of methods
for multi-class support vector machines
</A>,
<I>IEEE Transactions on Neural Networks</A></I>, 13(2002), 415-425.
<p>
"1-against-the rest" is a good method whose performance
is comparable to "1-against-1." We do the latter
simply because its training time is shorter.
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<a name="f420"><b>Q: After doing cross validation, why there is no model file outputted ?</b></a>
<br/>
<p>
Cross validation is used for selecting good parameters.
After finding them, you want to re-train the whole
data without the -v option.
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<a name="f4201"><b>Q: Why my cross-validation results are different from those in the Practical Guide?</b></a>
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<p>
Due to random partitions of
the data, on different systems CV accuracy values
may be different.
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<a name="f421"><b>Q: But on some systems CV accuracy is the same in several runs. How could I use different data partitions?</b></a>
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<p>
If you use GNU C library,
the default seed 1 is considered. Thus you always
get the same result of running svm-train -v.
To have different seeds, you can add the following code
in svm-train.c:
<pre>
#include <time.h>
</pre>
and in the beginning of the subroutine do_cross_validation(),
<pre>
srand(time(0));
</pre>
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<a name="f422"><b>Q: I would like to solve L2-loss SVM (i.e., error term is quadratic). How should I modify the code ?</b></a>
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<p>
It is extremely easy. Taking c-svc for example, to solve
<p>
min_w w^Tw/2 + C \sum max(0, 1- (y_i w^Tx_i+b))^2,