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 ...

Get Pattern Recognition now with O’Reilly online learning.

O’Reilly members experience live online training, plus books, videos, and digital content from 200+ publishers.