March 2010
Intermediate to advanced
150 pages
7h 13m
English
book
Introduction to Pattern Recognition: A Matlab Approach
by Sergios Theodoridis, Aggelos Pikrakis, Konstantinos Koutroumbas, Dionisis Cavouras
Content preview from Introduction to Pattern Recognition: A Matlab Approach
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122 CHAPTER 4 Feature Selection
0
1
2
0
2
4
22
22
22
21
21
0
1
2
3
Skewness
Mean
Standard deviation
FIGURE 4.5
Plot of the (normalized) 3-feature combination (mean value, standard deviation, and skewness). This
combination leads to good class separation.
Work similarly to evaluate the J
3
for t he remaining 3-feature combinations:[1,2, 4], [1,3, 4], [2, 3,4].
The resul ts are 3.9742, 3.9741, 3.6195, and 1.3972 for the combinati ons [1, 2,3], [1,2, 4], [1,3, 4],
and [2, 3, 4], respectively.
Step 3. To plot the results of the feature combination [1,2,3], utilize the provided plotData function:
featureNames = {'mean','standart dev','skewness','kurtosis'};
plotData(class1,cla ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.