title: How can I compute the classifying function of svm in multiclass case manually without using the built in function in R?
body: <p>I know that  multi-class svm based on one versus one scheme is based on the voting method. The output of this sterategy is predicted label.
   I need to compute the amount of classification function , that in binary case, easily can be obtained by f(x)=wx+b. But my problem is how can I get f(x) for multi- class problem manullay. Is it similar to the binary problem that just need to extarct W and b from each problem then  compute f(x)?!
  By using ksvm function of kernlab, the "decision" argument give me the above mentioned amount. How can I produce this amount without using the predict function? Thank you!</p>
  
  <pre><code>irismodel &lt;- ksvm(Species~.,data=iris,type="C-bsvc",kernel=rbf,C=10, prob.model=TRUE)
  
  fx&lt;-predict(irismodel, newdata = iris[,-5], type = "decision")
  fx
  [1,] -1.3982580 -1.1850725 -4.22358404
  [2,] -1.2750649 -1.1486247 -4.22523983
  [3,] -1.4603977 -1.1910251 -3.88688364
  [4,] -1.3718116 -1.1492030 -3.78283230
  [5,] -1.4330830 -1.1910482 -3.93300606
  [6,] -1.1710980 -1.0910910 -3.83914172
  [7,] -1.4290407 -1.1480605 -3.70013283
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  </code></pre>