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TRANSCRIPT
Introduction What is SVM? SVM Applications
Text Categorization Face Detection
The Approach About the Program Test results Conclusions
CONTENTS
Although it constructs models that are complex, it is simple enough to be analyzed mathematically
It can lead to high performances in practical applications
ADVANTAGES
Text Categorization
An Example – Reuters
12,902 Reuters stories, 118 categories
75% to build classifiers
25% to test
SVM APPLICATIONS
Take several images for training (positive/negative)
Tresholding to separate the seed from background
Scale them and sub sample them to minimize the size of the vectors
Feed them to the learning machine model/classifier
THE APPROACH
training set: 28p – 23n
errors:
pos. images recognized as neg. 2-4%
neg. images recognized as pos. 1-2%
training set: 43p – 44n
errors:
pos. images recognized as neg. 0%
neg. images recognized as pos. 0%
TEST RESULTS
CONCLUSIONS
SVMs are a good choice for binary classification (see results in this case)
They can be used no matter what one may want to classify (faces, seeds, etc.)
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