forked from cjlin1/libsvm
-
Notifications
You must be signed in to change notification settings - Fork 0
/
FAQ.html
2070 lines (1942 loc) · 77.1 KB
/
FAQ.html
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
<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>
Thu, 20 Mar 2014 16:05:14 GMT
<class="categories">
<li><a
href="#_TOP">All Questions</a>(81)</li>
<ul><b>
<li><a
href="#/Q01:_Some_sample_uses_of_libsvm">Q01:_Some_sample_uses_of_libsvm</a>(2)</li>
<li><a
href="#/Q02:_Installation_and_running_the_program">Q02:_Installation_and_running_the_program</a>(13)</li>
<li><a
href="#/Q03:_Data_preparation">Q03:_Data_preparation</a>(7)</li>
<li><a
href="#/Q04:_Training_and_prediction">Q04:_Training_and_prediction</a>(28)</li>
<li><a
href="#/Q05:_Cross_validation_and_parameter_selection">Q05:_Cross_validation_and_parameter_selection</a>(8)</li>
<li><a
href="#/Q06:_Probability_outputs">Q06:_Probability_outputs</a>(3)</li>
<li><a
href="#/Q07:_Graphic_interface">Q07:_Graphic_interface</a>(3)</li>
<li><a
href="#/Q08:_Java_version_of_libsvm">Q08:_Java_version_of_libsvm</a>(4)</li>
<li><a
href="#/Q09:_Python_interface">Q09:_Python_interface</a>(1)</li>
<li><a
href="#/Q10:_MATLAB_interface">Q10:_MATLAB_interface</a>(12)</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/tools which have used libsvm </a></li>
<li class="headlines_item"><a href="#f201">Where can I find documents/videos 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">How to cite LIBSVM?</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="#f211">How to build a dynamic library (.dll file) on MS windows?</a></li>
<li class="headlines_item"><a href="#f212">On some systems (e.g., Ubuntu), compiling LIBSVM gives many warning messages. Is this a problem and how to disable the warning message?</a></li>
<li class="headlines_item"><a href="#f213">In LIBSVM, why you don't use certain C/C++ library functions to make the code shorter?</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="#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?</a></li>
<li class="headlines_item"><a href="#f418">I would like to use my own kernel. Any example? In svm.cpp, 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="#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="#f425">In one-class SVM, parameter nu should be an upper bound of the training error rate. Why sometimes I get a training error rate bigger than nu?</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="#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="#f434">Why sv_indices (indices of support vectors) are not stored in the saved model file?</a></li>
<li class="headlines_item"><a href="#f501">After doing cross validation, why there is no model file outputted ?</a></li>
<li class="headlines_item"><a href="#f502">Why my cross-validation results are different from those in the Practical Guide?</a></li>
<li class="headlines_item"><a href="#f503">On some systems CV accuracy is the same in several runs. How could I use different data partitions? In other words, how do I set random seed in LIBSVM?</a></li>
<li class="headlines_item"><a href="#f504">Why on windows sometimes grid.py fails?</a></li>
<li class="headlines_item"><a href="#f505">Why grid.py/easy.py sometimes generates the following warning message?</a></li>
<li class="headlines_item"><a href="#f506">How do I choose the kernel?</a></li>
<li class="headlines_item"><a href="#f507">How does LIBSVM perform parameter selection for multi-class problems? </a></li>
<li class="headlines_item"><a href="#f508">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="#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="#f704">Except the python-C++ interface provided, could I use Jython to call libsvm ?</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>
<li class="headlines_item"><a href="#f807">On Windows I got an error message "Invalid MEX-file: Specific module not found" when running the pre-built MATLAB interface in the windows sub-directory. What should I do?</a></li>
<li class="headlines_item"><a href="#f808">LIBSVM supports 1-vs-1 multi-class classification. If instead I would like to use 1-vs-rest, how to implement it using MATLAB interface?</a></li>
<li class="headlines_item"><a href="#f809">I tried to install matlab interface on mac, but failed. What should I do?</a></li>
</ul></ul>
<hr size="5" noshade />
<p/>
<a name="/Q01:_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="/Q01:_Some_sample_uses_of_libsvm"></a>
<a name="faq102"><b>Q: Some applications/tools which have used libsvm </b></a>
<br/>
(and maybe liblinear).
<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://bdval.campagnelab.org>
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>
<li><a href=http://scikit-learn.sourceforge.net/>
scikits.learn: machine learning in Python</a>
</li>
</ul>
<p align="right">
<a href="#_TOP">[Go Top]</a>
<hr/>
<a name="/Q02:_Installation_and_running_the_program"></a>
<a name="f201"><b>Q: Where can I find documents/videos of libsvm ?</b></a>
<br/>
<p>
<ul>
<li>
Official implementation document:
<br>
C.-C. Chang and
C.-J. Lin.
LIBSVM
: a library for support vector machines.
ACM Transactions on Intelligent
Systems and Technology, 2:27:1--27:27, 2011.
<a href="http://www.csie.ntu.edu.tw/~cjlin/papers/libsvm.pdf">pdf</a>, <a href=http://www.csie.ntu.edu.tw/~cjlin/papers/libsvm.ps.gz>ps.gz</a>,
<a href=http://portal.acm.org/citation.cfm?id=1961199&CFID=29950432&CFTOKEN=30974232>ACM digital lib</a>.
<li> Instructions for using LIBSVM are in the README files in the main directory and some sub-directories.
<br>
README in the main directory: details all options, data format, and library calls.
<br>
tools/README: parameter selection and other tools
<li>
A guide for beginners:
<br>
C.-W. Hsu, C.-C. Chang, and
C.-J. Lin.
<A HREF="http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf">
A practical guide to support vector classification
</A>
<li> An <a href=http://www.youtube.com/watch?v=gePWtNAQcK8>introductory video</a>
for windows users.
</ul>
<p align="right">
<a href="#_TOP">[Go Top]</a>
<hr/>
<a name="/Q02:_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="/Q02:_Installation_and_running_the_program"></a>
<a name="f203"><b>Q: How to cite LIBSVM?</b></a>
<br/>
<p>
Please cite the following paper:
<p>
Chih-Chung Chang and Chih-Jen Lin, LIBSVM
: a library for support vector machines.
ACM Transactions on Intelligent Systems and Technology, 2:27:1--27:27, 2011.
Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm
<p>
The bibtex format is
<pre>
@article{CC01a,
author = {Chang, Chih-Chung and Lin, Chih-Jen},
title = {{LIBSVM}: A library for support vector machines},
journal = {ACM Transactions on Intelligent Systems and Technology},
volume = {2},
issue = {3},
year = {2011},
pages = {27:1--27:27},
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="/Q02:_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.
Please check the COPYRIGHT file in detail. Basically
you need to
<ol>
<li>
Clearly indicate that LIBSVM is used.
</li>
<li>
Retain the LIBSVM COPYRIGHT file in your software.
</li>
</ol>
It can also be used in commercial products.
<p align="right">
<a href="#_TOP">[Go Top]</a>
<hr/>
<a name="/Q02:_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="/Q02:_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="/Q02:_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="/Q02:_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="/Q02:_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">
<a href="#_TOP">[Go Top]</a>
<hr/>
<a name="/Q02:_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.
<p align="right">
<a href="#_TOP">[Go Top]</a>
<hr/>
<a name="/Q02:_Installation_and_running_the_program"></a>
<a name="f211"><b>Q: How to build a dynamic library (.dll file) on MS windows?</b></a>
<br/>
<p>
The easiest way is to use Makefile.win.
See details in README.
Alternatively, you can use Visual C++. Here is
the example using Visual Studio .Net 2008:
<ol>
<li>Create a Win32 empty DLL project and set (in Project->$Project_Name
Properties...->Configuration) to "Release."
About how to create a new dynamic link library, please refer to
<a href=http://msdn2.microsoft.com/en-us/library/ms235636(VS.80).aspx>http://msdn2.microsoft.com/en-us/library/ms235636(VS.80).aspx</a>
<li> Add svm.cpp, svm.h to your project.
<li> Add __WIN32__ and _CRT_SECURE_NO_DEPRECATE to Preprocessor definitions (in
Project->$Project_Name Properties...->C/C++->Preprocessor)
<li> Set Create/Use Precompiled Header to Not Using Precompiled Headers
(in Project->$Project_Name Properties...->C/C++->Precompiled Headers)
<li> Set the path for the Modulation Definition File svm.def (in
Project->$Project_Name Properties...->Linker->input
<li> Build the DLL.
<li> Rename the dll file to libsvm.dll and move it to the correct path.
</ol>
<p align="right">
<a href="#_TOP">[Go Top]</a>
<hr/>
<a name="/Q02:_Installation_and_running_the_program"></a>
<a name="f212"><b>Q: On some systems (e.g., Ubuntu), compiling LIBSVM gives many warning messages. Is this a problem and how to disable the warning message?</b></a>
<br/>
<p>
If you are using a version before 3.18, probably you see
a warning message like
<pre>
svm.cpp:2730: warning: ignoring return value of int fscanf(FILE*, const char*, ...), declared with attribute warn_unused_result
</pre>
This is not a problem; see <a href=https://wiki.ubuntu.com/CompilerFlags#-D_FORTIFY_SOURCE=2>this page</a> for more
details of ubuntu systems.
To disable the warning message you can replace
<pre>
CFLAGS = -Wall -Wconversion -O3 -fPIC
</pre>
with
<pre>
CFLAGS = -Wall -Wconversion -O3 -fPIC -U_FORTIFY_SOURCE
</pre>
in Makefile.
<p> After version 3.18, we have a better setting so that such warning messages do not appear.
<p align="right">
<a href="#_TOP">[Go Top]</a>
<hr/>
<a name="/Q02:_Installation_and_running_the_program"></a>
<a name="f213"><b>Q: In LIBSVM, why you don't use certain C/C++ library functions to make the code shorter?</b></a>
<br/>
<p>
For portability, we use only features defined in ISO C89. Note that features in ISO C99 may not be available everywhere.
Even the newest gcc lacks some features in C99 (see <a href=http://gcc.gnu.org/c99status.html>http://gcc.gnu.org/c99status.html</a> for details).
If the situation changes in the future,
we might consider using these newer features.
<p align="right">
<a href="#_TOP">[Go Top]</a>
<hr/>
<a name="/Q03:_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="/Q03:_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="/Q03:_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="/Q03:_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">
<a href="#_TOP">[Go Top]</a>
<hr/>
<a name="/Q03:_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">
<a href="#_TOP">[Go Top]</a>
<hr/>
<a name="/Q03:_Data_preparation"></a>
<a name="f306"><b>Q: May I put comments in data files?</b></a>
<br/>
<p>
We don't officially support this. But, currently LIBSVM
is able to process data in the following
format:
<pre>
1 1:2 2:1 # your comments
</pre>
Note that the character ":" should not appear in your
comments.
<!--
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>
-->
<p align="right">
<a href="#_TOP">[Go Top]</a>
<hr/>
<a name="/Q03:_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. A simple way is to use
libsvmwrite in the libsvm matlab/octave interface.
Take a CSV (comma-separated values) 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.
<p>
Alternatively, you can use <a href="./faqfiles/convert.c">convert.c</a>
to convert CSV format to libsvm format.
<p align="right">
<a href="#_TOP">[Go Top]</a>
<hr/>
<a name="/Q04:_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">
<a href="#_TOP">[Go Top]</a>
<hr/>
<a name="/Q04:_Training_and_prediction"></a>
<a name="f402"><b>Q: Can you explain more about the model file?</b></a>
<br/>
<p>
In the model file, after parameters and other informations such as labels , each line represents a support vector.
Support vectors are listed in the order of "labels" shown 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>
<p align="right">
<a href="#_TOP">[Go Top]</a>
<hr/>
<a name="/Q04:_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">
<a href="#_TOP">[Go Top]</a>
<hr/>
<a name="/Q04:_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.
<p align="right">
<a href="#_TOP">[Go Top]</a>
<hr/>
<a name="/Q04:_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.
<p align="right">
<a href="#_TOP">[Go Top]</a>
<hr/>
<a name="/Q04:_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>
<p align="right">
<a href="#_TOP">[Go Top]</a>
<hr/>
<a name="/Q04:_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">
<a href="#_TOP">[Go Top]</a>
<hr/>
<a name="/Q04:_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 tools
directory find
out good parameters. To see the importance of model selection,
please
see our guide for beginners:
<A HREF="http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf">
A practical guide to support vector
classification
</A>
<p align="right">
<a href="#_TOP">[Go Top]</a>
<hr/>
<a name="/Q04:_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.
<p align="right">
<a href="#_TOP">[Go Top]</a>
<hr/>
<a name="/Q04:_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.
<p align="right">
<a href="#_TOP">[Go Top]</a>
<hr/>
<a name="/Q04:_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.
<p align="right">
<a href="#_TOP">[Go Top]</a>
<hr/>
<a name="/Q04:_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.
<p>
You may get better numerical stability by replacing
<pre>
typedef float Qfloat;
</pre>
in svm.cpp with
<pre>
typedef double Qfloat;
</pre>
That is, elements in the kernel cache are stored
in double instead of single. However, this means fewer elements
can be put in the kernel cache.
<p align="right">
<a href="#_TOP">[Go Top]</a>
<hr/>
<a name="/Q04:_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">
<a href="#_TOP">[Go Top]</a>
<hr/>
<a name="/Q04:_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">
<a href="#_TOP">[Go Top]</a>
<hr/>
<a name="/Q04:_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>
If you are using MATLAB/OCTAVE interface, svmpredict can directly
give you decision values. Please see matlab/README for details.
<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">
<a href="#_TOP">[Go Top]</a>
<hr/>
<a name="/Q04:_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">
<a href="#_TOP">[Go Top]</a>
<hr/>
<a name="/Q04:_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">
<a href="#_TOP">[Go Top]</a>
<hr/>
<a name="/Q04:_Training_and_prediction"></a>
<a name="f417"><b>Q: How do I disable screen output of svm-train?</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>