Tutorial Support Vector Machine

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16 Οκτ 2013 (πριν από 3 χρόνια και 11 μήνες)

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Tutorial


Support Vector Machine



Support Vector Machine (SVM) is a very popular technique for data
classification. It finds the optimal separation boundary by searching the
maximum margin between the classes.


















To
give

the

computer a
rtificial intelligence
, we would like the computer to be
able to classify things into different categories. The first job is to teach the
computer what the target objects are. We usually take the discriminative
attributes

of the target objects first, such
as colour, shape, regions in
computer vision. The extracted data will form a data set called
training set
.
SVM will take the training set for a
supervised learning

and find the optimal
boundary to separate different classes.


The next job is to classify ne
w coming data. This step is usually called
testing
. A new
sample will be classified into a class by judging what side of
the boundary the sample point sits on.



Tutorial tasks



1.

Examine the two videos in the SVM.zip file
.

2.

read libsvm website and download
the libsvm

http://www.csie.ntu.edu.tw/~cjlin/libsvm/

3.

scale the training set and testing set by svm
-
scale.exe

svm
-
scale.exe
-
l
-
1
-
u 1
-
s range train.avi_dataset_dim_576.libsvm >
train.scaled

svm
-
sca
le.exe
-
r range test.avi_dataset_dim_576.libsvm > test.scaled

4.

train SVM

svm
-
train.exe train.scaled

5.

classification

svm
-
predict.exe test.scaled train.scaled.model predict.txt > result.txt

6.

for more details, read “a practical guide to svm classification” pdf
f
ile

7.

copy the

returned
predict file

into the
appropriate

SVM solution folder

8.

compile and run the SVM solution.