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Pattern Recognition by Matthias Nagel, Matthias Richter, Jürgen Beyerer

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and the decision boundaries are given by

P( ω 1 |m )>P( ω 1 |m ) lnP( ω 1 |m )>lnP( ω 1 |m ) lnP( ω 1 |m )+lnP( ω 1 )>lnP( ω 1 |m )+lnP( ω 1 ),( 3.50 )

The decision boundaries are hyperplanes that are perpendicular to the connection lines between the expectation. If the a priori probabilities of the classes are equal, i.e., ln P( ω i ) P( ω j ) =0, then the hyperplanes lie at the center points between the expectation vectors. If the a priori probabilities are not equal, the hyperplanes move toward the component with lower a priori probability. Examples with one and two features are shown in Figure 3.10.

For the second step, continue ...

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