faq.html

LIBSVM 是台湾大学林智仁(Chih-Jen Lin)博士等开发设计的一个操作简单、易于使用、快速有效的通用SVM 软件包

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<a name="f302"><b>Q: What if my data are non-numerical ?</b></a>
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<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.
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<a name="f303"><b>Q: Why do you consider sparse format ? Will the training of dense data be much slower ?</b></a>
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<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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<a name="f304"><b>Q: Why sometimes the last line of my data is not read by svm-train?</b></a>
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<p>
We assume that you have '\n' in the end of
each line. So please press enter in the end
of your last line.
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<a name="f305"><b>Q: Is there a program to check if my data are in the correct format?</b></a>
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<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.
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<a name="f306"><b>Q: May I put comments in data files?</b></a>
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<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
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<a name="f401"><b>Q: The output of training C-SVM is like the following. What do they mean?</b></a>
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<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>.
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<a name="f402"><b>Q: Can you explain more about the model file?</b></a>
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<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 each support vector, 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|                    |
+-+-+-+--------------------+
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<a name="f403"><b>Q: Should I use float or double to store numbers in the cache ?</b></a>
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<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.
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<a name="f404"><b>Q: How do I choose the kernel?</b></a>
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<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.ps.gz>
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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<a name="f405"><b>Q: Does libsvm have special treatments for linear SVM?</b></a>
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<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.ps.gz">
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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<a name="f406"><b>Q: The number of free support vectors is large. What should I do?</b></a>
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 <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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<a name="f407"><b>Q: Should I scale training and testing data in a similar way?</b></a>
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<p>
Yes, you can do the following:
<br> svm-scale -s scaling_parameters train_data > scaled_train_data
<br> svm-scale -r scaling_parameters test_data > scaled_test_data
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<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>
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<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.
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<a name="f409"><b>Q: The prediction rate is low. How could I improve it?</b></a>
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<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>
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<a name="f410"><b>Q: My data are unbalanced. Could libsvm handle such problems?</b></a>
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<p>
Yes, there is a -wi options. For example, if you use
<p>
 svm-train -s 0 -c 10 -w1 1 -w-1 5 data_file
<p>
the penalty for class "-1" is larger.
Note that this -w option is for C-SVC only.
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