In every book and example always they show only binary classification (two classes) and new vector can belong to any one class.
Here the problem is I have 4 classes(c1, c2, c3, c4). I've training data for 4 classes.
For new vector the output should be like
C1 80% (the winner)
c2 10%
c3 6%
c4 4%
How to do this? I'm planning to use libsvm (because it most popular). I don't know much about it. If any of you guys used it previously please tell me specific commands I'm supposed to use.
You can always reduce a multi-class classification problem to a binary problem by choosing random partititions of the set of classes, recursively. This is not necessarily any less effective or efficient than learning all at once, since the sub-learning problems require less examples since the partitioning problem is smaller. (It may require at most a constant order time more, e.g. twice as long). It may also lead to more accurate learning.
I'm not necessarily recommending this, but it is one answer to your question, and is a general technique that can be applied to any binary learning algorithm.
It does not have a specific switch (command) for multi-class prediction. it automatically handles multi-class prediction if your training dataset contains more than two classes.
Nothing special compared with binary prediction. see the following example for 3-class prediction based on SVM.
Use the SVM Multiclass library. Find it at the SVM page by Thorsten Joachims
LibSVM uses the one-against-one approach for multi-class learning problems. From the FAQ: