Pattern Recognition 1: Introduction - KTH
Pattern Recognition 1: Introduction - KTH
Pattern Recognition 1: Introduction - KTH
Create successful ePaper yourself
Turn your PDF publications into a flip-book with our unique Google optimized e-Paper software.
Classification<br />
Summary<br />
Intro<br />
Example<br />
Probability Density<br />
Gaussian Probability Density (2-dim)<br />
5<br />
x 2<br />
4<br />
3<br />
2<br />
1<br />
0<br />
−1<br />
−2<br />
−3<br />
−4<br />
−5<br />
−5 −4 −3 −2 −1 0 1 2 3 4 5<br />
x 1<br />
✓ ◆ 3<br />
2<br />
0<br />
cov [X ]=P<br />
0 1 2 P T =<br />
✓ ◆ 5 4<br />
=<br />
4 5<br />
where<br />
P = p 1 ✓ ◆ 1 1<br />
2 1 1<br />
Arne Leijon<br />
<strong>Pattern</strong> <strong>Recognition</strong> 1: <strong>Introduction</strong>