LECTURE THEME : - Discriminant Functions
Discriminant Functions: one way of representing classifiers
Given the classes $ \omega_1, \cdots, \omega_k $
The discriminant functions $ g_1(x),\ldots, g_K(x) $ such that $ g_i(x) $ n-dim S space $ \rightarrow \Re $
which are used to make decisions as follows:
decide $ \omega_i $ if $ g_i(x) \ge g_j(x), \forall j $
Note that many different choices of $ g_i(x) $ will yield the same decision rule, because we are interested in the order of values of $ g_i(x) $ for each x, and not their exact values.
For example: $ g_i(x) \rightarrow 2(g_i(x)) $ or $ g_i(x) \rightarrow ln(g_i(x)) $
In other words, we can take $ g_i(x) \rightarrow f(g_i(x)) $ for any monotonically increasing function f.
Relation to Bayes Rule
e.g. We can take $ g_i(\mathbf(x)) = P(\omega_i|\mathbf(x)) $
then $ g_i(\mathbf(x)) > g_j(\mathbf(x)), \forall j \neq i $
$ \Longleftrightarrow P(w_i|\mathbf(X)) > P(w_j|\mathbf(X)), \forall j \neq i $
OR we can take
$ g_i(\mathbf(x)) = p(\mathbf(x)|\omega_i)P(\omega_i) $
then $ g_i(\mathbf(x)) > g_j(\mathbf(x)), \forall j \neq i $
$ \Longleftrightarrow g_i(\mathbf(x)) = ln(p(\mathbf(x)|\omega_i)P(\omega_i)) = ln(p(\mathbf(x)|\omega_i))+ln(P(\omega_i) $
