@@ -332,13 +332,13 @@ to classify new data.
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For example, if we have the following training data:
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- LABEL ATTR1 ATTR2 ATTR3 ATTR4 ATTR5
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- ----- ----- ----- ----- ----- -----
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- 1 0 0.1 0.2 0 0
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- 2 0 0.1 0.3 -1.2 0
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- 1 0.4 0 0 0 0
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- 2 0 0.1 0 1.4 0.5
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- 3 -0.1 -0.2 0.1 1.1 0.1
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+ LABEL ATTR1 ATTR2 ATTR3 ATTR4 ATTR5
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+ ----- ----- ----- ----- ----- -----
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+ 1 0 0.1 0.2 0 0
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+ 2 0 0.1 0.3 -1.2 0
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+ 1 0.4 0 0 0 0
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+ 2 0 0.1 0 1.4 0.5
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+ 3 -0.1 -0.2 0.1 1.1 0.1
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then the components of svm_problem are:
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@@ -347,10 +347,10 @@ to classify new data.
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y -> 1 2 1 2 3
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x -> [ ] -> (2,0.1) (3,0.2) (-1,?)
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- [ ] -> (2,0.1) (3,0.3) (4,-1.2) (-1,?)
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- [ ] -> (1,0.4) (-1,?)
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- [ ] -> (2,0.1) (4,1.4) (5,0.5) (-1,?)
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- [ ] -> (1,-0.1) (2,-0.2) (3,0.1) (4,1.1) (5,0.1) (-1,?)
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+ [ ] -> (2,0.1) (3,0.3) (4,-1.2) (-1,?)
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+ [ ] -> (1,0.4) (-1,?)
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+ [ ] -> (2,0.1) (4,1.4) (5,0.5) (-1,?)
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+ [ ] -> (1,-0.1) (2,-0.2) (3,0.1) (4,1.1) (5,0.1) (-1,?)
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where (index,value) is stored in the structure `svm_node':
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