MATLAB Support Vector Machine Toolbox
=====================================
(c) Dr Gavin Cawley, September 2000.
This is a (slightly less) beta version of a MATLAB toolbox implementing
Vapnik's support vector machine, as described in [1]. The toolbox currently
supports multi-class pattern recognition, Platt's sequential minimal
optimisation algorithm [2] and an efficient estimate of the leave-oe-out
cross-validation error [3]. The SMO training algorithm is implemented as a
mex file (for speed), and a .mexlx file for Linux machines is supplied. At
the moment this is the only documentation for the toolbox but the file demo.m
provides a simple demonstration that ought to be enough to get started. Key
features:
(a) C++ MEX implementation of the SMO training algorithm, with caching of
kernel evaluations for efficiency.
(b) Support for multi-class pattern recognition.
(c) An efficient criterion for model selection.
(d) Object oriented design, currently this just means that you can supply
bespoke kernel functions for particular applications, but will in future
releases also support a range of training algorithms, model selection
criteria etc.
LICENSING ARRANGEMENTS
======================
The toolbox is provided free for non-commercial use under the terms of the
GNU GPL licence (see licence.txt in this directory), however, I would be
grateful if:
(a) you let me know about any bugs you find,
(b) you send suggestions of ideas to improve the toolbox (e.g.
references to other training algorithms),
(c) reference the toolbox web page in any publication describing research
performed using the toolbox, or software derived from the toolbox. A
suitable BibTeX entry would look something like this:
@misc{Cawley2000,
author = "Cawley, G. C.",
title = "{MATLAB} Support Vector Machine Toolbox (v0.50$\beta$) $[$
\texttt{http://theoval.sys.uea.ac.uk/\~{}gcc/svm/toolbox}$]$",
howpublished = "University of East Anglia, School of Information Systems,
Norwich, Norfolk, U.K. NR4 7TJ",
year = 2000
}
TO DO LIST
==========
1. Find time to write a proper list of things to do!
2. Documentation.
3. Support Vector Regression.
4. Automated model selection.
REFERENCES
==========
[1] V.N. Vapnik,
"The Nature of Statistical Learning Theory",
Springer-Verlag, New York, ISBN 0-387-94559-8,
1995.
[2] J. C. Platt,
"Fast training of support vector machines using sequential minimal
optimization", in Advances in Kernel Methods - Support Vector Learning,
(Eds) B. Scholkopf, C. Burges, and A. J. Smola, MIT Press, Cambridge,
Massachusetts, chapter 12, pp 185-208, 1999.
[3] T. Joachims, "Estimating the Generalization Performance of a SVM
Efficiently", LS-8 Report 25, Universitat Dortmund, Fachbereich
Informatik, 1999.
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SVMmatlab.rar_SVMmatlab程序_svmmatlab (206个子文件)
manual.aux 1KB
manual.bbl 1KB
manual.bib 4KB
manual.blg 996B
apalike.bst 22KB
pr_loqo.c 16KB
pr_loqo.c 16KB
qp.c 7KB
loqo.c 7KB
evaluate.c 3KB
code14-11 319B
SmoTutor.cpp 10KB
LrrCache.cpp 4KB
smosvctrain.cpp 4KB
InfCache.cpp 2KB
mexSVMTrain.dll 72KB
smosvctrain.dll 68KB
mexSVMClass.dll 60KB
qp.dll 48KB
qp.dll 48KB
evaluate.dll 40KB
readme.doc 25KB
manual.dvi 7KB
uea_logo.gif 7KB
new.gif 147B
bbar_lft.gif 116B
bbar_rgt.gif 116B
bbar_mid.gif 106B
black.gif 70B
ecoc-codes.tar.gz 309KB
SmoTutor.h 3KB
LrrCache.h 2KB
pr_loqo.h 2KB
pr_loqo.h 2KB
InfCache.h 2KB
Cache.h 2KB
utils.hh 1KB
svminfo.htm 9KB
MATLAB Support Vector Machine Toolbox.htm 7KB
OSU SVM Classifier Matlab Toolbox.htm 5KB
Anton Schwaighofer Software.htm 5KB
smosvctrain.ilk 138KB
svc.jpg 35KB
svr.jpg 33KB
background.jpg 7KB
isis_small.jpg 2KB
manual.log 5KB
svmtrain.m 21KB
demsvm2.m 11KB
svmcv.m 8KB
demsvm1.m 8KB
SVMTest.m 7KB
SVMClass.m 6KB
SVMPlot2.m 6KB
uiregress.m 5KB
SVMTrain.m 5KB
uiclass.m 5KB
mexSVMClass.m 5KB
svm.m 5KB
mexSVMTrain.m 4KB
svmstat.m 4KB
demo.m 4KB
SVMPlot.m 4KB
svr.m 4KB
kpca_toy.m 3KB
one_rbfdemo.m 3KB
u_poldemo.m 3KB
c_poldemo.m 3KB
u_rbfdemo.m 3KB
c_rbfdemo.m 3KB
ecocload.m 3KB
dagsvmdemo.m 3KB
svcplot.m 3KB
train.m 3KB
u_PolySVC.m 3KB
xialpha.m 3KB
Contents.m 3KB
u_lindemo.m 3KB
c_lindemo.m 3KB
PolySVC.m 3KB
svmkernel.m 3KB
svc.m 3KB
ecoctrain.m 3KB
svc.m 3KB
svkernel.m 3KB
consist.m 3KB
fwd.m 2KB
one_RbfSVC.m 2KB
u_RbfSVC.m 2KB
train.m 2KB
RbfSVC.m 2KB
rbf.m 2KB
pairwise.m 2KB
svmfwd.m 2KB
maxwin.m 2KB
dagsvm.m 2KB
polynomial.m 2KB
fixduplicates.m 2KB
train.m 2KB
evaluate.m 2KB
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