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One way to represent classifiers. Given classes w1, w2, .., wk and feature vector x, which is in n-dimensional space, the discriminant functions g1(x), g2(x), .., gk(x) where g#(x) maps n-dimensional space to real numbers, are used to make decisions. Decisions are defined as w# if g#(x) >= gj(x) for all j.
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One way to represent classifiers.
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Given classes <math>w_1, w_2, \ldots, w_k</math> and feature vector x, which is in n-dimensional space, the discriminant functions <math>g_1(x), g_2(x), \ldots, g_k(x)</math> where <math>g_\#(x)</math> maps n-dimensional space to real numbers, are used to make decisions. Decisions are defined as <math>w_\#</math> if <math>g_\#(x) >= g_j(x)</math> for all j.
  
 
Discriminant functions are used to define [[Decision Surfaces_Old Kiwi]].
 
Discriminant functions are used to define [[Decision Surfaces_Old Kiwi]].

Latest revision as of 09:53, 10 April 2008

One way to represent classifiers. Given classes $ w_1, w_2, \ldots, w_k $ and feature vector x, which is in n-dimensional space, the discriminant functions $ g_1(x), g_2(x), \ldots, g_k(x) $ where $ g_\#(x) $ maps n-dimensional space to real numbers, are used to make decisions. Decisions are defined as $ w_\# $ if $ g_\#(x) >= g_j(x) $ for all j.

Discriminant functions are used to define Decision Surfaces_Old Kiwi.

See also

Linear Discriminant Functions_Old Kiwi

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Seraj Dosenbach