dagsvmdemo.m ( File view )

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% Demonstrate the use of the Support Vector Machine toolbox to distinguish
% examples of Versicolour, Setosa and Virginica varieties of Iris, given
% petal and sepal width and length attributes.  The data is taken from the
% well known Iris benchmark [1], available from the UCI Repository of Machine
% Learning % Databases (http://www.ics.uci.edu/~mlearn/MLRepository.html).
% [1] R. A. Fisher,
%     "The use of multiple measurements in taxonomic problems",
%     Annual Eugenics, 7(2), pp 179-188, 1936.

% File        : dagsvmdemo.m
% Date        : Friday 24th November 2000
% Author      : Dr Gavin C. Cawley
% Description : Test harness for object oriented implementation of Vapnik's
%               linear support vector machine (SVM) [1].  This file tests the
%               dag-svm algorithm for multi-class pattern recognition.
% References  : [1] V.N. Vapnik,
%                   "The Nature of Statistical Learning Theory",
%                   Springer-Verlag, New York, ISBN 0-387-94559-8,
%                   1995.
% History     : 14/11/1999 - v1.00 
% Copyright   : (c) Dr Gavin C. Cawley, November 2000.
%    This program is free software; you can redistribute it and/or modify
%    it under the terms of the GNU General Public License as published by
%    the Free Software Foundation; either version 2 of the License, or
%    (at your option) any later version.
%    This program is distributed in the hope that it will be useful,
%    but WITHOUT ANY WARRANTY; without even the implied warranty of
%    GNU General Public License for more details.
%    You should have received a copy of the GNU General Public License
%    along with this program; if not, write to the Free Software
%    Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA

% start from a clean slate

clear classes all;

% load some data

fprintf(1,'loading training data...\n');

iris = load('data/iris.txt');

% the data matrix contains n columns where n is the number of
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Name Size Date
compilemex.m314.00 B11-03-03|16:49
correctness.m1.72 kB16-09-00|20:26
dagsvmdemo.m3.14 kB11-03-03|14:58
demo.m4.12 kB12-02-03|13:20
licence.txt17.92 kB16-09-00|20:26
readme.txt2.93 kB16-09-00|20:32
display.m1.56 kB16-09-00|20:26
evaluate.m1.85 kB16-09-00|20:26
char.m1.66 kB16-09-00|20:26
polynomial.m2.23 kB16-09-00|20:26
@polynomial0.00 B22-04-06|08:46
fwd.m1.99 kB28-11-00|11:16
train.m2.40 kB28-11-00|11:16
pairwise.m2.32 kB16-09-00|20:26
@pairwise0.00 B22-04-06|08:46
fwd.m1.77 kB16-09-00|20:26
train.m2.05 kB16-09-00|20:26
maxwin.m2.32 kB16-09-00|20:26
@maxwin0.00 B22-04-06|08:46
display.m1.53 kB16-09-00|20:26
evaluate.m1.71 kB16-09-00|20:26
char.m1.55 kB16-09-00|20:26
linear.m1.93 kB16-09-00|20:26
@linear0.00 B22-04-06|08:46
fwd.m2.50 kB28-11-00|11:16
getnsv.m1.66 kB11-03-03|14:54
train.m2.12 kB28-11-00|11:16
dagsvm.m2.29 kB16-09-00|20:26
@dagsvm0.00 B22-04-06|08:46
manual.bbl1.14 kB16-09-00|20:26
manual.bib3.99 kB16-09-00|20:26
manual.aux1.21 kB16-09-00|20:26
manual.blg996.00 B16-09-00|20:26
manual.dvi7.30 kB16-09-00|20:26
manual.log5.05 kB16-09-00|20:26
manual.tex4.40 kB16-09-00|20:26
Makefile313.00 B16-09-00|20:26
manual.ps84.40 kB16-09-00|20:26
apalike.bst22.38 kB16-09-00|20:26
apalike.sty1.09 kB16-09-00|20:26
doc0.00 B22-04-06|08:46
iris.txt2.79 kB16-09-00|20:26
iris.names2.62 kB24-11-00|07:47
data0.00 B22-04-06|08:46
svctutor.m1.96 kB16-09-00|20:26
@svctutor0.00 B22-04-06|08:46
display.m2.01 kB16-09-00|20:26
fwd.m1.77 kB16-09-00|20:26
svc.m2.58 kB16-09-00|20:26
getkernel.m1.61 kB16-09-00|20:26
compact.m2.04 kB16-09-00|20:26
getbias.m1.59 kB16-09-00|20:26
.xialpha.m.swp12.00 kB16-09-00|20:29
strip.m2.00 kB16-09-00|20:26
getw.m1.55 kB16-09-00|20:26
getsv.m1.58 kB16-09-00|20:26
fixduplicates.m2.19 kB16-09-00|20:26
getnsv.m1.60 kB16-09-00|20:26
train.m1.71 kB16-09-00|20:26
xialpha.m2.87 kB16-09-00|20:26
@svc0.00 B22-04-06|08:46
InfCache.cpp2.34 kB16-09-00|20:26
smosvctutor.m2.06 kB16-09-00|20:26
Cache.h2.14 kB16-09-00|20:26
InfCache.h2.22 kB16-09-00|20:26
LrrCache.cpp4.28 kB17-11-00|13:34
compilemex.m1.76 kB31-01-03|09:19
SmoTutor.h2.75 kB16-09-00|20:26
smosvctrain.mexglx17.00 kB11-03-03|16:49
train.m3.02 kB16-09-00|20:26
SmoTutor.cpp10.02 kB17-11-00|13:33
LrrCache.h2.41 kB17-11-00|13:30
smosvctrain.cpp4.10 kB16-09-00|20:26
utils.hh1.45 kB16-09-00|20:26
@smosvctutor0.00 B22-04-06|08:46
r.m1.59 kB16-09-00|20:26
display.m1.56 kB16-09-00|20:26
rbf.m2.33 kB16-09-00|20:26
evaluate.c2.71 kB16-09-00|20:26
evaluate.m2.09 kB16-09-00|20:26
compilemex.m1.57 kB31-01-03|09:15
char.m1.67 kB16-09-00|20:26
evaluate.mexglx62.54 kB11-03-03|16:49
@rbf0.00 B22-04-06|08:46
svm_v0.55beta0.00 B22-04-06|08:46
binomial.m371.00 B19-09-97|08:35
centrefig.m144.00 B01-05-98|11:47
cmap.mat1.69 kB13-08-97|15:33
Contents.m1.08 kB07-08-98|16:24
newsvm.zip74.74 kB26-10-01|14:23
nobias.m457.00 B06-08-98|16:39
qp.dll48.00 kB26-10-01|14:21
README2.58 kB12-10-01|15:27
softmargin.m312.00 B21-04-98|21:25
svc.m2.62 kB21-08-98|12:03
svcerror.m837.00 B21-08-98|11:04
svcinfo.m1.20 kB10-03-98|16:14
svcoutput.m973.00 B21-04-98|21:24
svcplot.m3.04 kB12-10-01|01:50
svdatanorm.m1.27 kB23-06-98|11:09
svkernel.m2.55 kB11-10-01|15:44
svr.m3.89 kB21-08-98|15:36
svrerror.m1.17 kB21-08-98|10:33
svroutput.m711.00 B15-04-98|23:05
svrplot.m1.78 kB13-02-98|10:31
svtol.m401.00 B21-08-98|14:57
uiclass.m5.26 kB18-11-97|17:15
uiclass.mat12.30 kB18-11-97|17:15
uiregress.m5.50 kB27-09-97|22:42
uiregress.mat11.37 kB12-10-98|13:24
Makefile27.00 B11-10-01|15:14
pr_loqo.c16.34 kB11-10-01|15:14
pr_loqo.h2.33 kB11-10-01|15:14
qp.c7.08 kB11-10-01|15:14
qp.dll48.00 kB26-10-01|14:21
Optimiser0.00 B22-04-06|08:46
example.mat744.00 B07-11-97|15:15
sinc.mat1.03 kB20-08-97|15:01
titanium.mat1.07 kB27-09-97|23:30
Regression0.00 B22-04-06|08:46
iris1v23.mat2.63 kB28-09-97|16:24
iris2v13.mat2.63 kB28-09-97|16:25
iris3v12.mat2.63 kB28-09-97|16:25
linsep.mat672.00 B06-11-97|15:46
nlinsep.mat712.00 B06-11-97|15:49
Classification0.00 B22-04-06|08:46
Examples0.00 B22-04-06|08:46
svm(推荐)0.00 B22-04-06|08:46
SVM工具箱0.00 B22-04-06|08:46
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